
R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> source("pscore_estimation_core1.R")
Loading required package: gbm
Loading required package: survival
Loading required package: splines
Loading required package: lattice
Loaded gbm 1.6-3.1 
Loading required package: survey

Attaching package: 'survey'

The following object(s) are masked from 'package:graphics':

    dotchart

Loading required package: xtable
Loading required package: MASS
Loading required package: mlbench
Loading required package: nnet
Loading required package: class

Attaching package: 'ipred'

The following object(s) are masked from 'package:survey':

    cv

randomForest 4.5-36
Type rfNews() to see new features/changes/bug fixes.
> library(Matching)
Loading required package: rgenoud
##  rgenoud (Version 5.6-7, Build Date: 2010-06-01)
##  See http://sekhon.berkeley.edu/rgenoud for additional documentation.
## 
##  Matching (Version 4.7-10, Build Date: 2010/08/13)
##  See http://sekhon.berkeley.edu/matching for additional documentation.
##  Please cite software as:
##   Jasjeet S. Sekhon. Forthcoming. ``Multivariate and Propensity Score Matching
##   Software with Automated Balance Optimization: The Matching package for R.''
##   Journal of Statistical Software. 
##
> 
> options(warn=1)
> 
> load(file="RData.simdata.E.obs1000.reps1000")
> dta <- simdata.E.obs1000.reps1000
> rm(simdata.E.obs1000.reps1000)
> gc()
           used (Mb) gc trigger  (Mb) max used (Mb)
Ncells   200676 10.8     407500  21.8   350000 18.7
Vcells 12195008 93.1   13300699 101.5 12195652 93.1
> 
> #true estimate
> g1 <- -0.4
> 
> sims <- dim(dta)[2]
> nobs <- length(dta[1,2]$w1)
> 
> true.est <- matrix(0, nrow=sims, ncol=1)
> 
> logit.pscore <- list()
> logit.est <- matrix(0, nrow=sims, ncol=1)
> logit.nSE <- matrix(0, nrow=sims, ncol=1)
> logit.aiSE <- matrix(0, nrow=sims, ncol=1)
> 
> boosting.pscore <- list()
> boosting.desc <- list()
> boosting.est <- matrix(0, nrow=sims, ncol=1)
> boosting.nSE <- matrix(0, nrow=sims, ncol=1)
> boosting.aiSE <- matrix(0, nrow=sims, ncol=1)
> 
> forest.pscore <- list()
> forest.est <- matrix(0, nrow=sims, ncol=1)
> forest.nSE <- matrix(0, nrow=sims, ncol=1)
> forest.aiSE <- matrix(0, nrow=sims, ncol=1)
> 
> nnet.pscore <- list()
> nnet.est <- matrix(0, nrow=sims, ncol=1)
> nnet.nSE <- matrix(0, nrow=sims, ncol=1)
> nnet.aiSE <- matrix(0, nrow=sims, ncol=1)
> 
> count <- 0
> for (s in 1:sims)
+   {
+     cat("\n\ns:",s,"\n")
+     count <- count+1
+ 
+     sdta <- as.data.frame(dta[,s])
+ 
+     #true model
+ #    lm1 <- lm(y.a~ z.a+ w1 + w2 + w3 + w4 + w8 + w9 + w10, data=sdta)
+ #    true.est[s] <- lm1$coef[2]
+ #    cat("truth:",mean(true.est[1:s]),"\n")
+ 
+     #logit
+     logit.ps <- glm(z.a ~ w1+w2+w3+w4+w5+w6+w7+w8+w9+w10, data=sdta, family=binomial(link="logit"))$fitted
+     logit.pscore[[s]] <- logit.ps
+     m1 <- Match(Y=sdta$y.a, Tr=sdta$z.a, X=logit.ps, estimand="ATT", M=1)
+     logit.est[s] <-  m1$est
+     logit.nSE[s] <- m1$se.standard
+     logit.aiSE[s] <- m1$se
+ 
+     cat("logit",s,":",m1$est,"\n")
+     if(s>1)
+       {
+         cat("logit mean",s,":",mean(logit.est[1:s]),"\n")
+         rmse <- sqrt(mean((logit.est[1:s]-g1)^2))
+         cat("logit RMSE",s,":",rmse,"\n")
+       }
+ 
+     #boosting!
+     ps.model <- ps(z.a ~ w1+w2+w3+w4+w5+w6+w7+w8+w9+w10,
+                    data=sdta, title="none", stop.method=stop.methods["ks.stat.mean"],
+                    plots="none", pdf.plots=F, n.trees=20000, interaction.depth=4,
+                    shrinkage=0.0005, verbose=FALSE)
+ 
+     boosting.pscore[[s]] <- ps.model$ps
+     boosting.desc[[s]] <- ps.model$desc
+ 
+     m1 <- Match(Y=sdta$y.a, Tr=sdta$z.a, X=ps.model$ps, estimand="ATT", M=1)
+     boosting.est[s] <-  m1$est
+     boosting.nSE[s] <- m1$se.standard
+     boosting.aiSE[s] <- m1$se
+ 
+     cat("\nboosting",s,":",m1$est,"\n")
+     if(s>1)
+       {
+         cat("boosting mean",s,":",mean(boosting.est[1:s]),"\n")
+         rmse <- sqrt(mean((boosting.est[1:s]-g1)^2))
+         cat("boosting RMSE",s,":",rmse,"\n")
+       }    
+ 
+ 
+     #Random Forest
+     ps1 <- 1
+     while (max(ps1)> .999) {
+ #      gc(verbose=TRUE)
+       fps.model <- randomForest(as.factor(z.a) ~ w1+w2+w3+w4+w5+w6+w7+w8+w9+w10, data=sdta)
+       ps1 <- fps.model$votes[, 2]
+     }
+     
+     forest.pscore[[s]] <- ps1
+ 
+     m1 <- Match(Y=sdta$y.a, Tr=sdta$z.a, X=ps1, estimand="ATT", M=1)
+     forest.est[s] <-  m1$est
+     forest.nSE[s] <- m1$se.standard
+     forest.aiSE[s] <- m1$se
+ 
+     cat("\nforest",s,":",m1$est,"\n")
+     if(s>1)
+       {
+         cat("forest mean",s,":",mean(forest.est[1:s]),"\n")
+         rmse <- sqrt(mean((forest.est[1:s]-g1)^2))
+         cat("forest RMSE",s,":",rmse,"\n")
+       }
+ 
+     n1 <- nnet(z.a ~ w1+w2+w3+w4+w5+w6+w7+w8+w9+w10, size=40, data=sdta, trace=FALSE)
+     nnet.pscore[[s]] <- n1$fitted.values
+ 
+     m1 <- Match(Y=sdta$y.a, Tr=sdta$z.a, X=n1$fitted.values, estimand="ATT", M=1)
+     nnet.est[s] <-  m1$est
+     nnet.nSE[s] <- m1$se.standard
+     nnet.aiSE[s] <- m1$se
+ 
+     cat("\nnnet",s,":",m1$est,"\n")
+     if(s>1)
+       {
+         cat("nnet mean",s,":",mean(nnet.est[1:s]),"\n")
+         rmse <- sqrt(mean((nnet.est[1:s]-g1)^2))
+         cat("nnet RMSE",s,":",rmse,"\n")
+       }    
+ 
+     if (count>50)
+       {
+         save.image("RData.sims.E.nobs1000")
+         count <- 0
+       }
+   }#end of sims loop


s: 1 
logit 1 : -0.5028284 

boosting 1 : -0.489394 

forest 1 : -0.3787593 

nnet 1 : -0.6390827 


s: 2 
logit 2 : -0.4272273 
logit mean 2 : -0.4650279 
logit RMSE 2 : 0.07521636 

boosting 2 : -0.5153503 
boosting mean 2 : -0.5023721 
boosting RMSE 2 : 0.1031915 

forest 2 : -0.3935801 
forest mean 2 : -0.3861697 
forest RMSE 2 : 0.01569052 

nnet 2 : -0.4855688 
nnet mean 2 : -0.5623257 
nnet RMSE 2 : 0.1795585 


s: 3 
logit 3 : -0.4286241 
logit mean 3 : -0.4528933 
logit RMSE 3 : 0.06359859 

boosting 3 : -0.5398105 
boosting mean 3 : -0.5148516 
boosting RMSE 3 : 0.1166818 

forest 3 : -0.4267538 
forest mean 3 : -0.3996977 
forest RMSE 3 : 0.02006781 

nnet 3 : -0.3817968 
nnet mean 3 : -0.5021494 
nnet RMSE 3 : 0.1469851 


s: 4 
logit 4 : -0.4764216 
logit mean 4 : -0.4587753 
logit RMSE 4 : 0.06703469 

boosting 4 : -0.3463374 
boosting mean 4 : -0.4727231 
boosting RMSE 4 : 0.1045510 

forest 4 : -0.4349383 
forest mean 4 : -0.4085079 
forest RMSE 4 : 0.02464162 

nnet 4 : -0.5342237 
nnet mean 4 : -0.510168 
nnet RMSE 4 : 0.1439009 


s: 5 
logit 5 : -0.4536924 
logit mean 5 : -0.4577588 
logit RMSE 5 : 0.0645871 

boosting 5 : -0.4634872 
boosting mean 5 : -0.4708759 
boosting RMSE 5 : 0.09772846 

forest 5 : -0.3119947 
forest mean 5 : -0.3892052 
forest RMSE 5 : 0.04510826 

nnet 5 : -0.3682255 
nnet mean 5 : -0.4817795 
nnet RMSE 5 : 0.1294909 


s: 6 
logit 6 : -0.3718382 
logit mean 6 : -0.4434387 
logit RMSE 6 : 0.06007017 

boosting 6 : -0.2959032 
boosting mean 6 : -0.4417138 
boosting RMSE 6 : 0.09881835 

forest 6 : -0.3346116 
forest mean 6 : -0.3801063 
forest RMSE 6 : 0.04907378 

nnet 6 : -0.4717874 
nnet mean 6 : -0.4801141 
nnet RMSE 6 : 0.1217873 


s: 7 
logit 7 : -0.3815725 
logit mean 7 : -0.4346006 
logit RMSE 7 : 0.05604861 

boosting 7 : -0.4324104 
boosting mean 7 : -0.4403847 
boosting RMSE 7 : 0.09230449 

forest 7 : -0.5262428 
forest mean 7 : -0.4009829 
forest RMSE 7 : 0.06588591 

nnet 7 : -0.5169967 
nnet mean 7 : -0.4853831 
nnet RMSE 7 : 0.1211146 


s: 8 
logit 8 : -0.4767493 
logit mean 8 : -0.4398692 
logit RMSE 8 : 0.05903451 

boosting 8 : -0.4458499 
boosting mean 8 : -0.4410679 
boosting RMSE 8 : 0.08785147 

forest 8 : -0.4456192 
forest mean 8 : -0.4065625 
forest RMSE 8 : 0.06370614 

nnet 8 : -0.2456764 
nnet mean 8 : -0.4554197 
nnet RMSE 8 : 0.1257462 


s: 9 
logit 9 : -0.5153813 
logit mean 9 : -0.4482594 
logit RMSE 9 : 0.06765388 

boosting 9 : -0.4870956 
boosting mean 9 : -0.4461821 
boosting RMSE 9 : 0.0877678 

forest 9 : -0.4017154 
forest mean 9 : -0.4060239 
forest RMSE 9 : 0.06006545 

nnet 9 : -0.1628321 
nnet mean 9 : -0.42291 
nnet RMSE 9 : 0.1424958 


s: 10 
logit 10 : -0.4384028 
logit mean 10 : -0.4472738 
logit RMSE 10 : 0.0653209 

boosting 10 : -0.4156444 
boosting mean 10 : -0.4431283 
boosting RMSE 10 : 0.08341069 

forest 10 : -0.3918535 
forest mean 10 : -0.4046069 
forest RMSE 10 : 0.05704129 

nnet 10 : -0.2093146 
nnet mean 10 : -0.4015505 
nnet RMSE 10 : 0.1480224 


s: 11 
logit 11 : -0.3861184 
logit mean 11 : -0.4417142 
logit RMSE 11 : 0.06242152 

boosting 11 : -0.5385748 
boosting mean 11 : -0.4518052 
boosting RMSE 11 : 0.08983642 

forest 11 : -0.3502459 
forest mean 11 : -0.399665 
forest RMSE 11 : 0.05641773 

nnet 11 : -0.5066575 
nnet mean 11 : -0.4111057 
nnet RMSE 11 : 0.1447513 


s: 12 
logit 12 : -0.3669412 
logit mean 12 : -0.4354831 
logit RMSE 12 : 0.0605212 

boosting 12 : -0.3107324 
boosting mean 12 : -0.4400492 
boosting RMSE 12 : 0.08978915 

forest 12 : -0.2993257 
forest mean 12 : -0.3913034 
forest RMSE 12 : 0.06133778 

nnet 12 : -0.3944943 
nnet mean 12 : -0.4097214 
nnet RMSE 12 : 0.1385979 


s: 13 
logit 13 : -0.4316479 
logit mean 13 : -0.4351881 
logit RMSE 13 : 0.05880566 

boosting 13 : -0.4935466 
boosting mean 13 : -0.4441644 
boosting RMSE 13 : 0.09008375 

forest 13 : -0.3550703 
forest mean 13 : -0.3885162 
forest RMSE 13 : 0.06023451 

nnet 13 : -0.3559098 
nnet mean 13 : -0.405582 
nnet RMSE 13 : 0.1337209 


s: 14 
logit 14 : -0.4469737 
logit mean 14 : -0.4360299 
logit RMSE 14 : 0.05804057 

boosting 14 : -0.5303289 
boosting mean 14 : -0.450319 
boosting RMSE 14 : 0.09353445 

forest 14 : -0.3916060 
forest mean 14 : -0.3887369 
forest RMSE 14 : 0.05808677 

nnet 14 : -0.3646678 
nnet mean 14 : -0.4026596 
nnet RMSE 14 : 0.1292022 


s: 15 
logit 15 : -0.4347284 
logit mean 15 : -0.4359432 
logit RMSE 15 : 0.05678495 

boosting 15 : -0.4592265 
boosting mean 15 : -0.4509128 
boosting RMSE 15 : 0.09164769 

forest 15 : -0.3203206 
forest mean 15 : -0.3841758 
forest RMSE 15 : 0.05976946 

nnet 15 : -0.4617496 
nnet mean 15 : -0.4065989 
nnet RMSE 15 : 0.1258353 


s: 16 
logit 16 : -0.4871537 
logit mean 16 : -0.4391438 
logit RMSE 16 : 0.05914164 

boosting 16 : -0.7344587 
boosting mean 16 : -0.4686344 
boosting RMSE 16 : 0.1219252 

forest 16 : -0.3717021 
forest mean 16 : -0.3833962 
forest RMSE 16 : 0.05830233 

nnet 16 : -0.4270202 
nnet mean 16 : -0.4078752 
nnet RMSE 16 : 0.1220266 


s: 17 
logit 17 : -0.3819044 
logit mean 17 : -0.4357768 
logit RMSE 17 : 0.05754343 

boosting 17 : -0.3872053 
boosting mean 17 : -0.4638445 
boosting RMSE 17 : 0.1183255 

forest 17 : -0.3803808 
forest mean 17 : -0.3832188 
forest RMSE 17 : 0.05676137 

nnet 17 : -0.2632397 
nnet mean 17 : -0.3993673 
nnet RMSE 17 : 0.1229422 


s: 18 
logit 18 : -0.4336613 
logit mean 18 : -0.4356593 
logit RMSE 18 : 0.05648218 

boosting 18 : -0.6296646 
boosting mean 18 : -0.4730567 
boosting RMSE 18 : 0.1270961 

forest 18 : -0.4016626 
forest mean 18 : -0.3842435 
forest RMSE 18 : 0.05516353 

nnet 18 : -0.6185746 
nnet mean 18 : -0.4115455 
nnet RMSE 18 : 0.1301124 


s: 19 
logit 19 : -0.3815782 
logit mean 19 : -0.4328129 
logit RMSE 19 : 0.05513793 

boosting 19 : -0.5005681 
boosting mean 19 : -0.4745047 
boosting RMSE 19 : 0.1258394 

forest 19 : -0.4428081 
forest mean 19 : -0.3873258 
forest RMSE 19 : 0.05458302 

nnet 19 : -0.335206 
nnet mean 19 : -0.4075276 
nnet RMSE 19 : 0.1275115 


s: 20 
logit 20 : -0.2837659 
logit mean 20 : -0.4253605 
logit RMSE 20 : 0.05969673 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 20 : -0.469141 
boosting mean 20 : -0.4742365 
boosting RMSE 20 : 0.1236236 

forest 20 : -0.3948472 
forest mean 20 : -0.3877019 
forest RMSE 20 : 0.05321342 

nnet 20 : -0.4408716 
nnet mean 20 : -0.4091948 
nnet RMSE 20 : 0.1246184 


s: 21 
logit 21 : -0.4054671 
logit mean 21 : -0.4244132 
logit RMSE 21 : 0.05827026 

boosting 21 : -0.3240533 
boosting mean 21 : -0.4670849 
boosting RMSE 21 : 0.1217773 

forest 21 : -0.3358697 
forest mean 21 : -0.3852337 
forest RMSE 21 : 0.05378354 

nnet 21 : -0.4764718 
nnet mean 21 : -0.4123985 
nnet RMSE 21 : 0.1227547 


s: 22 
logit 22 : -0.4727657 
logit mean 22 : -0.4266111 
logit RMSE 22 : 0.05900644 

boosting 22 : -0.3437789 
boosting mean 22 : -0.4614801 
boosting RMSE 22 : 0.1195797 

forest 22 : -0.4513857 
forest mean 22 : -0.3882406 
forest RMSE 22 : 0.05367687 

nnet 22 : -0.372605 
nnet mean 22 : -0.4105897 
nnet RMSE 22 : 0.1200745 


s: 23 
logit 23 : -0.4595526 
logit mean 23 : -0.4280433 
logit RMSE 23 : 0.0590303 

boosting 23 : -0.4259714 
boosting mean 23 : -0.4599362 
boosting RMSE 23 : 0.1170766 

forest 23 : -0.4370813 
forest mean 23 : -0.3903641 
forest RMSE 23 : 0.05306336 

nnet 23 : -0.2878216 
nnet mean 23 : -0.4052519 
nnet RMSE 23 : 0.119742 


s: 24 
logit 24 : -0.4330203 
logit mean 24 : -0.4282507 
logit RMSE 24 : 0.05817917 

boosting 24 : -0.4875466 
boosting mean 24 : -0.4610867 
boosting RMSE 24 : 0.1159964 

forest 24 : -0.5115772 
forest mean 24 : -0.3954147 
forest RMSE 24 : 0.05671972 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 24 : -0.1786948 
nnet mean 24 : -0.3958120 
nnet RMSE 24 : 0.125624 


s: 25 
logit 25 : -0.4336461 
logit mean 25 : -0.4284665 
logit RMSE 25 : 0.05739953 

boosting 25 : -0.4538478 
boosting mean 25 : -0.4607971 
boosting RMSE 25 : 0.1141619 

forest 25 : -0.3399848 
forest mean 25 : -0.3931975 
forest RMSE 25 : 0.05685521 

nnet 25 : -0.2158207 
nnet mean 25 : -0.3886124 
nnet RMSE 25 : 0.1284796 


s: 26 
logit 26 : -0.4947514 
logit mean 26 : -0.4310159 
logit RMSE 26 : 0.05927299 

boosting 26 : -0.4143186 
boosting mean 26 : -0.4590095 
boosting RMSE 26 : 0.1119801 

forest 26 : -0.3566123 
forest mean 26 : -0.3917903 
forest RMSE 26 : 0.05639673 

nnet 26 : -0.4117172 
nnet mean 26 : -0.389501 
nnet RMSE 26 : 0.1260056 


s: 27 
logit 27 : -0.4945196 
logit mean 27 : -0.4333679 
logit RMSE 27 : 0.06094303 

boosting 27 : -0.577762 
boosting mean 27 : -0.4634077 
boosting RMSE 27 : 0.1150890 

forest 27 : -0.439151 
forest mean 27 : -0.3935444 
forest RMSE 27 : 0.05585304 

nnet 27 : -0.2211604 
nnet mean 27 : -0.3832662 
nnet RMSE 27 : 0.1283508 


s: 28 
logit 28 : -0.3915100 
logit mean 28 : -0.431873 
logit RMSE 28 : 0.05986637 

boosting 28 : -0.6410912 
boosting mean 28 : -0.4697536 
boosting RMSE 28 : 0.1218536 

forest 28 : -0.3296871 
forest mean 28 : -0.3912638 
forest RMSE 28 : 0.0564333 

nnet 28 : -0.6700965 
nnet mean 28 : -0.3935101 
nnet RMSE 28 : 0.1359817 


s: 29 
logit 29 : -0.4090795 
logit mean 29 : -0.431087 
logit RMSE 29 : 0.05884929 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 29 : -0.6075118 
boosting mean 29 : -0.4745038 
boosting RMSE 29 : 0.1257822 

forest 29 : -0.4109822 
forest mean 29 : -0.3919438 
forest RMSE 29 : 0.05548926 

nnet 29 : -0.5144046 
nnet mean 29 : -0.3976789 
nnet RMSE 29 : 0.1352949 


s: 30 
logit 30 : -0.501626 
logit mean 30 : -0.4334383 
logit RMSE 30 : 0.06076231 

boosting 30 : -0.4288940 
boosting mean 30 : -0.4729835 
boosting RMSE 30 : 0.1237805 

forest 30 : -0.4500947 
forest mean 30 : -0.3938821 
forest RMSE 30 : 0.05531792 

nnet 30 : -0.3041907 
nnet mean 30 : -0.3945626 
nnet RMSE 30 : 0.1341661 


s: 31 
logit 31 : -0.5020628 
logit mean 31 : -0.435652 
logit RMSE 31 : 0.06252189 

boosting 31 : -0.5614463 
boosting mean 31 : -0.4758372 
boosting RMSE 31 : 0.1251726 

forest 31 : -0.3392810 
forest mean 31 : -0.3921208 
forest RMSE 31 : 0.05550035 

nnet 31 : -0.6974946 
nnet mean 31 : -0.4043346 
nnet RMSE 31 : 0.1423897 


s: 32 
logit 32 : -0.5390409 
logit mean 32 : -0.4388829 
logit RMSE 32 : 0.06626438 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 32 : -0.5355451 
boosting mean 32 : -0.477703 
boosting RMSE 32 : 0.1255097 

forest 32 : -0.3909915 
forest mean 32 : -0.3920855 
forest RMSE 32 : 0.05464948 

nnet 32 : -0.0790432 
nnet mean 32 : -0.3941693 
nnet RMSE 32 : 0.1511965 


s: 33 
logit 33 : -0.5122501 
logit mean 33 : -0.4411062 
logit RMSE 33 : 0.06811555 

boosting 33 : -0.3266405 
boosting mean 33 : -0.4731254 
boosting RMSE 33 : 0.1242514 

forest 33 : -0.4097014 
forest mean 33 : -0.3926193 
forest RMSE 33 : 0.05384158 

nnet 33 : -0.2834070 
nnet mean 33 : -0.3908128 
nnet RMSE 33 : 0.1502651 


s: 34 
logit 34 : -0.4224559 
logit mean 34 : -0.4405576 
logit RMSE 34 : 0.0672168 

boosting 34 : -0.4260436 
boosting mean 34 : -0.4717406 
boosting RMSE 34 : 0.122492 

forest 34 : -0.3505967 
forest mean 34 : -0.3913834 
forest RMSE 34 : 0.05371628 

nnet 34 : -0.4274089 
nnet mean 34 : -0.3918892 
nnet RMSE 34 : 0.1481134 


s: 35 
logit 35 : -0.3992045 
logit mean 35 : -0.4393761 
logit RMSE 35 : 0.06624973 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 35 : -0.4534393 
boosting mean 35 : -0.4712177 
boosting RMSE 35 : 0.1210669 

forest 35 : -0.4278557 
forest mean 35 : -0.3924254 
forest RMSE 35 : 0.0531523 

nnet 35 : -0.4245074 
nnet mean 35 : -0.3928212 
nnet RMSE 35 : 0.1460409 


s: 36 
logit 36 : -0.4340268 
logit mean 36 : -0.4392275 
logit RMSE 36 : 0.06556883 

boosting 36 : -0.5889748 
boosting mean 36 : -0.4744888 
boosting RMSE 36 : 0.1234586 

forest 36 : -0.3626328 
forest mean 36 : -0.3915979 
forest RMSE 36 : 0.05277761 

nnet 36 : -0.6293897 
nnet mean 36 : -0.3993925 
nnet RMSE 36 : 0.1489871 


s: 37 
logit 37 : -0.2977879 
logit mean 37 : -0.4354048 
logit RMSE 37 : 0.0668239 

boosting 37 : -0.1786239 
boosting mean 37 : -0.4664924 
boosting RMSE 37 : 0.1271008 

forest 37 : -0.4246053 
forest mean 37 : -0.3924900 
forest RMSE 37 : 0.05221644 

nnet 37 : -0.4443238 
nnet mean 37 : -0.4006069 
nnet RMSE 37 : 0.1471405 


s: 38 
logit 38 : -0.5569818 
logit mean 38 : -0.4386042 
logit RMSE 38 : 0.07068543 

boosting 38 : -0.3032049 
boosting mean 38 : -0.4621954 
boosting RMSE 38 : 0.1263964 

forest 38 : -0.4628313 
forest mean 38 : -0.3943410 
forest RMSE 38 : 0.05252326 

nnet 38 : -0.6847053 
nnet mean 38 : -0.4080831 
nnet RMSE 38 : 0.1523604 


s: 39 
logit 39 : -0.3024185 
logit mean 39 : -0.4351123 
logit RMSE 39 : 0.07150157 

boosting 39 : -0.5053103 
boosting mean 39 : -0.4633009 
boosting RMSE 39 : 0.1258998 

forest 39 : -0.4526571 
forest mean 39 : -0.3958363 
forest RMSE 39 : 0.0525267 

nnet 39 : -0.3877702 
nnet mean 39 : -0.4075623 
nnet RMSE 39 : 0.1504071 


s: 40 
logit 40 : -0.3664671 
logit mean 40 : -0.4333961 
logit RMSE 40 : 0.07080095 

boosting 40 : -0.5564637 
boosting mean 40 : -0.4656299 
boosting RMSE 40 : 0.1267538 

forest 40 : -0.3480389 
forest mean 40 : -0.3946414 
forest RMSE 40 : 0.05251264 

nnet 40 : -0.4565639 
nnet mean 40 : -0.4087873 
nnet RMSE 40 : 0.1487841 


s: 41 
logit 41 : -0.4340271 
logit mean 41 : -0.4334115 
logit RMSE 41 : 0.07013381 

boosting 41 : -0.3636861 
boosting mean 41 : -0.4631435 
boosting RMSE 41 : 0.1253269 

forest 41 : -0.4198268 
forest mean 41 : -0.3952557 
forest RMSE 41 : 0.05196063 

nnet 41 : -0.2427161 
nnet mean 41 : -0.4047368 
nnet RMSE 41 : 0.1489972 


s: 42 
logit 42 : -0.4316881 
logit mean 42 : -0.4333705 
logit RMSE 42 : 0.06946615 

boosting 42 : -0.2898218 
boosting mean 42 : -0.4590168 
boosting RMSE 42 : 0.1249875 

forest 42 : -0.2738015 
forest mean 42 : -0.3923639 
forest RMSE 42 : 0.05490733 

nnet 42 : -0.3993232 
nnet mean 42 : -0.4046079 
nnet RMSE 42 : 0.1472128 


s: 43 
logit 43 : -0.4693046 
logit mean 43 : -0.4342062 
logit RMSE 43 : 0.0694624 

boosting 43 : -0.4084631 
boosting mean 43 : -0.4578411 
boosting RMSE 43 : 0.1235324 

forest 43 : -0.2628494 
forest mean 43 : -0.3893519 
forest RMSE 43 : 0.05815627 

nnet 43 : -0.2651683 
nnet mean 43 : -0.4013651 
nnet RMSE 43 : 0.1469367 


s: 44 
logit 44 : -0.5161711 
logit mean 44 : -0.436069 
logit RMSE 44 : 0.07086668 

boosting 44 : -0.2464151 
boosting mean 44 : -0.453036 
boosting RMSE 44 : 0.1242961 

forest 44 : -0.3863449 
forest mean 44 : -0.3892836 
forest RMSE 44 : 0.05752845 

nnet 44 : -0.478782 
nnet mean 44 : -0.4031246 
nnet RMSE 44 : 0.1457421 


s: 45 
logit 45 : -0.3729296 
logit mean 45 : -0.4346659 
logit RMSE 45 : 0.07019095 

boosting 45 : -0.4740927 
boosting mean 45 : -0.4535039 
boosting RMSE 45 : 0.1234026 

forest 45 : -0.395299 
forest mean 45 : -0.3894173 
forest RMSE 45 : 0.05688997 

nnet 45 : -0.1543535 
nnet mean 45 : -0.3975964 
nnet RMSE 45 : 0.1486933 


s: 46 
logit 46 : -0.5240242 
logit mean 46 : -0.4366085 
logit RMSE 46 : 0.07179176 

boosting 46 : -0.4327037 
boosting mean 46 : -0.4530517 
boosting RMSE 46 : 0.1221491 

forest 46 : -0.3382595 
forest mean 46 : -0.3883051 
forest RMSE 46 : 0.0569998 

nnet 46 : -0.3452305 
nnet mean 46 : -0.396458 
nnet RMSE 46 : 0.1472897 


s: 47 
logit 47 : -0.4582881 
logit mean 47 : -0.4370698 
logit RMSE 47 : 0.071531 

boosting 47 : -0.5383551 
boosting mean 47 : -0.4548667 
boosting RMSE 47 : 0.1225162 

forest 47 : -0.318061 
forest mean 47 : -0.3868106 
forest RMSE 47 : 0.05764288 

nnet 47 : -0.3712698 
nnet mean 47 : -0.3959221 
nnet RMSE 47 : 0.1457746 


s: 48 
logit 48 : -0.4187930 
logit mean 48 : -0.436689 
logit RMSE 48 : 0.07083391 

boosting 48 : -0.4757404 
boosting mean 48 : -0.4553016 
boosting RMSE 48 : 0.1217252 

forest 48 : -0.3915889 
forest mean 48 : -0.3869101 
forest RMSE 48 : 0.05705219 

nnet 48 : -0.7516149 
nnet mean 48 : -0.4033323 
nnet RMSE 48 : 0.1529157 


s: 49 
logit 49 : -0.4647207 
logit mean 49 : -0.4372611 
logit RMSE 49 : 0.07071444 

boosting 49 : -0.5019381 
boosting mean 49 : -0.4562533 
boosting RMSE 49 : 0.1213536 

forest 49 : -0.3068045 
forest mean 49 : -0.3852753 
forest RMSE 49 : 0.05801533 

nnet 49 : -0.386493 
nnet mean 49 : -0.4029887 
nnet RMSE 49 : 0.1513596 


s: 50 
logit 50 : -0.4218940 
logit mean 50 : -0.4369537 
logit RMSE 50 : 0.07007216 

boosting 50 : -0.3482983 
boosting mean 50 : -0.4540942 
boosting RMSE 50 : 0.1203562 

forest 50 : -0.4212422 
forest mean 50 : -0.3859947 
forest RMSE 50 : 0.05751076 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 50 : -0.2659111 
nnet mean 50 : -0.4002471 
nnet RMSE 50 : 0.1510335 


s: 51 
logit 51 : -0.3863257 
logit mean 51 : -0.435961 
logit RMSE 51 : 0.0694082 

boosting 51 : -0.4794936 
boosting mean 51 : -0.4545923 
boosting RMSE 51 : 0.1196892 

forest 51 : -0.3051717 
forest mean 51 : -0.3844099 
forest RMSE 51 : 0.05847184 

nnet 51 : -0.3387651 
nnet mean 51 : -0.3990416 
nnet RMSE 51 : 0.1497911 


s: 52 
logit 52 : -0.4454195 
logit mean 52 : -0.4361429 
logit RMSE 52 : 0.06902554 

boosting 52 : -0.7778947 
boosting mean 52 : -0.4608096 
boosting RMSE 52 : 0.1296003 

forest 52 : -0.3216126 
forest mean 52 : -0.3832023 
forest RMSE 52 : 0.05891835 

nnet 52 : -0.4448491 
nnet mean 52 : -0.3999225 
nnet RMSE 52 : 0.1484741 


s: 53 
logit 53 : -0.4397142 
logit mean 53 : -0.4362103 
logit RMSE 53 : 0.06858854 

boosting 53 : -0.4194072 
boosting mean 53 : -0.4600284 
boosting RMSE 53 : 0.1283995 

forest 53 : -0.4317609 
forest mean 53 : -0.3841185 
forest RMSE 53 : 0.05852271 

nnet 53 : -0.5138501 
nnet mean 53 : -0.4020721 
nnet RMSE 53 : 0.1478959 


s: 54 
logit 54 : -0.5069938 
logit mean 54 : -0.4375211 
logit RMSE 54 : 0.0694929 

boosting 54 : -0.3494121 
boosting mean 54 : -0.45798 
boosting RMSE 54 : 0.1273913 

forest 54 : -0.3767731 
forest mean 54 : -0.3839824 
forest RMSE 54 : 0.05806439 

nnet 54 : -0.4206233 
nnet mean 54 : -0.4024156 
nnet RMSE 54 : 0.1465470 


s: 55 
logit 55 : -0.412948 
logit mean 55 : -0.4370743 
logit RMSE 55 : 0.06888038 

boosting 55 : -0.3912404 
boosting mean 55 : -0.4567665 
boosting RMSE 55 : 0.1262334 

forest 55 : -0.506664 
forest mean 55 : -0.386213 
forest RMSE 55 : 0.05930457 

nnet 55 : -0.4003363 
nnet mean 55 : -0.4023778 
nnet RMSE 55 : 0.1452086 


s: 56 
logit 56 : -0.4830818 
logit mean 56 : -0.4378959 
logit RMSE 56 : 0.06915955 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 56 : -0.6182722 
boosting mean 56 : -0.4596506 
boosting RMSE 56 : 0.1284565 

forest 56 : -0.3935115 
forest mean 56 : -0.3863433 
forest RMSE 56 : 0.05877908 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 56 : -0.2045899 
nnet mean 56 : -0.3988459 
nnet RMSE 56 : 0.1462562 


s: 57 
logit 57 : -0.46337 
logit mean 57 : -0.4383428 
logit RMSE 57 : 0.06906216 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 57 : -0.5815753 
boosting mean 57 : -0.4617896 
boosting RMSE 57 : 0.1295762 

forest 57 : -0.4136361 
forest mean 57 : -0.3868222 
forest RMSE 57 : 0.05828918 

nnet 57 : -0.3429147 
nnet mean 57 : -0.3978646 
nnet RMSE 57 : 0.1451647 


s: 58 
logit 58 : -0.4432786 
logit mean 58 : -0.4384279 
logit RMSE 58 : 0.06869965 

boosting 58 : -0.4571193 
boosting mean 58 : -0.4617091 
boosting RMSE 58 : 0.1286731 

forest 58 : -0.3946348 
forest mean 58 : -0.3869569 
forest RMSE 58 : 0.0577888 

nnet 58 : -0.4920417 
nnet mean 58 : -0.3994884 
nnet RMSE 58 : 0.1444144 


s: 59 
logit 59 : -0.4048154 
logit mean 59 : -0.4378582 
logit RMSE 59 : 0.06811785 

boosting 59 : -0.5296113 
boosting mean 59 : -0.46286 
boosting RMSE 59 : 0.1286891 

forest 59 : -0.3553677 
forest mean 59 : -0.3864215 
forest RMSE 59 : 0.05759085 

nnet 59 : -0.3298552 
nnet mean 59 : -0.3983082 
nnet RMSE 59 : 0.1434762 


s: 60 
logit 60 : -0.3935906 
logit mean 60 : -0.4371204 
logit RMSE 60 : 0.06755288 

boosting 60 : -0.4022815 
boosting mean 60 : -0.4618503 
boosting RMSE 60 : 0.1276125 

forest 60 : -0.3460176 
forest mean 60 : -0.3857481 
forest RMSE 60 : 0.05753256 

nnet 60 : -0.2713793 
nnet mean 60 : -0.3961927 
nnet RMSE 60 : 0.1432413 


s: 61 
logit 61 : -0.5250291 
logit mean 61 : -0.4385615 
logit RMSE 61 : 0.06888286 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 61 : -0.5149533 
boosting mean 61 : -0.4627209 
boosting RMSE 61 : 0.1274151 

forest 61 : -0.3665822 
forest mean 61 : -0.3854339 
forest RMSE 61 : 0.05721924 

nnet 61 : -0.3970177 
nnet mean 61 : -0.3962062 
nnet RMSE 61 : 0.1420628 


s: 62 
logit 62 : -0.4784417 
logit mean 62 : -0.4392048 
logit RMSE 62 : 0.06904754 

boosting 62 : -0.7877571 
boosting mean 62 : -0.4679634 
boosting RMSE 62 : 0.1356387 

forest 62 : -0.3423007 
forest mean 62 : -0.3847382 
forest RMSE 62 : 0.05722701 

nnet 62 : -0.4689685 
nnet mean 62 : -0.3973798 
nnet RMSE 62 : 0.1411845 


s: 63 
logit 63 : -0.4632474 
logit mean 63 : -0.4395864 
logit RMSE 63 : 0.06895928 

boosting 63 : -0.5714631 
boosting mean 63 : -0.4696062 
boosting RMSE 63 : 0.1362809 

forest 63 : -0.3914835 
forest mean 63 : -0.3848452 
forest RMSE 63 : 0.05678115 

nnet 63 : -0.3983648 
nnet mean 63 : -0.3973954 
nnet RMSE 63 : 0.1400596 


s: 64 
logit 64 : -0.4403551 
logit mean 64 : -0.4395984 
logit RMSE 64 : 0.06860412 

boosting 64 : -0.3704860 
boosting mean 64 : -0.4680575 
boosting RMSE 64 : 0.1352623 

forest 64 : -0.4661996 
forest mean 64 : -0.3861164 
forest RMSE 64 : 0.0569403 

nnet 64 : -0.1461325 
nnet mean 64 : -0.3934694 
nnet RMSE 64 : 0.1425384 


s: 65 
logit 65 : -0.3690984 
logit mean 65 : -0.4385138 
logit RMSE 65 : 0.06818217 

boosting 65 : -0.4434161 
boosting mean 65 : -0.4676784 
boosting RMSE 65 : 0.1343258 

forest 65 : -0.3791494 
forest mean 65 : -0.3860092 
forest RMSE 65 : 0.05655976 

nnet 65 : -0.3357684 
nnet mean 65 : -0.3925817 
nnet RMSE 65 : 0.1416619 


s: 66 
logit 66 : -0.5701893 
logit mean 66 : -0.4405089 
logit RMSE 66 : 0.07083239 

boosting 66 : -0.54378 
boosting mean 66 : -0.4688314 
boosting RMSE 66 : 0.134474 

forest 66 : -0.4160638 
forest mean 66 : -0.3864646 
forest RMSE 66 : 0.05616445 

nnet 66 : -0.3892026 
nnet mean 66 : -0.3925305 
nnet RMSE 66 : 0.1405909 


s: 67 
logit 67 : -0.4707485 
logit mean 67 : -0.4409602 
logit RMSE 67 : 0.07083113 

boosting 67 : -0.6286744 
boosting mean 67 : -0.4712172 
boosting RMSE 67 : 0.1363592 

forest 67 : -0.4153131 
forest mean 67 : -0.3868952 
forest RMSE 67 : 0.05577512 

nnet 67 : -0.4181571 
nnet mean 67 : -0.392913 
nnet RMSE 67 : 0.1395554 


s: 68 
logit 68 : -0.408821 
logit mean 68 : -0.4404876 
logit RMSE 68 : 0.07031652 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 68 : -0.428496 
boosting mean 68 : -0.4705889 
boosting RMSE 68 : 0.1353970 

forest 68 : -0.3255748 
forest mean 68 : -0.3859934 
forest RMSE 68 : 0.05609433 

nnet 68 : -0.3934769 
nnet mean 68 : -0.3929213 
nnet RMSE 68 : 0.1385277 


s: 69 
logit 69 : -0.4433533 
logit mean 69 : -0.4405291 
logit RMSE 69 : 0.06999996 

boosting 69 : -0.4119598 
boosting mean 69 : -0.4697392 
boosting RMSE 69 : 0.1344200 

forest 69 : -0.460103 
forest mean 69 : -0.3870674 
forest RMSE 69 : 0.05615447 

nnet 69 : -0.2132091 
nnet mean 69 : -0.3903168 
nnet RMSE 69 : 0.1393466 


s: 70 
logit 70 : -0.3426706 
logit mean 70 : -0.4391311 
logit RMSE 70 : 0.06983514 

boosting 70 : -0.412489 
boosting mean 70 : -0.4689214 
boosting RMSE 70 : 0.1334647 

forest 70 : -0.3589639 
forest mean 70 : -0.3866660 
forest RMSE 70 : 0.05596726 

nnet 70 : -0.3172627 
nnet mean 70 : -0.3892731 
nnet RMSE 70 : 0.1387007 


s: 71 
logit 71 : -0.3975098 
logit mean 71 : -0.4385449 
logit RMSE 71 : 0.06934223 

boosting 71 : -0.4068241 
boosting mean 71 : -0.4680467 
boosting RMSE 71 : 0.1325240 

forest 71 : -0.4658374 
forest mean 71 : -0.3877811 
forest RMSE 71 : 0.05611833 

nnet 71 : -0.4024246 
nnet mean 71 : -0.3894584 
nnet RMSE 71 : 0.1377207 


s: 72 
logit 72 : -0.3372634 
logit mean 72 : -0.4371382 
logit RMSE 72 : 0.0692548 

boosting 72 : -0.4550757 
boosting mean 72 : -0.4678666 
boosting RMSE 72 : 0.1317604 

forest 72 : -0.3467845 
forest mean 72 : -0.3872117 
forest RMSE 72 : 0.05607904 

nnet 72 : -0.1141534 
nnet mean 72 : -0.3856347 
nnet RMSE 72 : 0.1408489 


s: 73 
logit 73 : -0.482061 
logit mean 73 : -0.4377536 
logit RMSE 73 : 0.06944618 

boosting 73 : -0.3184294 
boosting mean 73 : -0.4658195 
boosting RMSE 73 : 0.1312026 

forest 73 : -0.4424981 
forest mean 73 : -0.387969 
forest RMSE 73 : 0.05591529 

nnet 73 : -0.4358559 
nnet mean 73 : -0.3863227 
nnet RMSE 73 : 0.1399438 


s: 74 
logit 74 : -0.4879091 
logit mean 74 : -0.4384314 
logit RMSE 74 : 0.06972827 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 74 : -0.5132876 
boosting mean 74 : -0.466461 
boosting RMSE 74 : 0.1309769 

forest 74 : -0.3460371 
forest mean 74 : -0.3874024 
forest RMSE 74 : 0.05588936 

nnet 74 : -0.5383751 
nnet mean 74 : -0.3883774 
nnet RMSE 74 : 0.1399227 


s: 75 
logit 75 : -0.4662342 
logit mean 75 : -0.4388021 
logit RMSE 75 : 0.06968284 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 75 : -0.5835226 
boosting mean 75 : -0.4680218 
boosting RMSE 75 : 0.1318153 

forest 75 : -0.4040965 
forest mean 75 : -0.3876249 
forest RMSE 75 : 0.05551753 

nnet 75 : -0.3514464 
nnet mean 75 : -0.387885 
nnet RMSE 75 : 0.1390998 


s: 76 
logit 76 : -0.4591599 
logit mean 76 : -0.4390699 
logit RMSE 76 : 0.06955472 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 76 : -0.702957 
boosting mean 76 : -0.471113 
boosting RMSE 76 : 0.1354782 

forest 76 : -0.346463 
forest mean 76 : -0.3870833 
forest RMSE 76 : 0.05549193 

nnet 76 : -0.4885004 
nnet mean 76 : -0.3892089 
nnet RMSE 76 : 0.1385540 


s: 77 
logit 77 : -0.3849896 
logit mean 77 : -0.4383676 
logit RMSE 77 : 0.06912275 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 77 : -0.5108984 
boosting mean 77 : -0.4716297 
boosting RMSE 77 : 0.1351876 

forest 77 : -0.3694641 
forest mean 77 : -0.3868545 
forest RMSE 77 : 0.05524013 

nnet 77 : -0.4037818 
nnet mean 77 : -0.3893982 
nnet RMSE 77 : 0.1376521 


s: 78 
logit 78 : -0.4196434 
logit mean 78 : -0.4381275 
logit RMSE 78 : 0.06871424 

boosting 78 : -0.49754 
boosting mean 78 : -0.4719619 
boosting RMSE 78 : 0.1347715 

forest 78 : -0.3755294 
forest mean 78 : -0.3867093 
forest RMSE 78 : 0.05495478 

nnet 78 : -0.5595304 
nnet mean 78 : -0.3915793 
nnet RMSE 78 : 0.1379545 


s: 79 
logit 79 : -0.3708182 
logit mean 79 : -0.4372755 
logit RMSE 79 : 0.06835684 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 79 : -0.313557 
boosting mean 79 : -0.4699568 
boosting RMSE 79 : 0.1342685 

forest 79 : -0.3933076 
forest mean 79 : -0.3867928 
forest RMSE 79 : 0.05461105 

nnet 79 : -0.1250707 
nnet mean 79 : -0.3882058 
nnet RMSE 79 : 0.1405252 


s: 80 
logit 80 : -0.4786419 
logit mean 80 : -0.4377926 
logit RMSE 80 : 0.06849494 

boosting 80 : -0.5250263 
boosting mean 80 : -0.4706452 
boosting RMSE 80 : 0.1341569 

forest 80 : -0.3472051 
forest mean 80 : -0.386298 
forest RMSE 80 : 0.05458872 

nnet 80 : -0.5330403 
nnet mean 80 : -0.3900162 
nnet RMSE 80 : 0.1404341 


s: 81 
logit 81 : -0.4723122 
logit mean 81 : -0.4382188 
logit RMSE 81 : 0.06854336 

boosting 81 : -0.256632 
boosting mean 81 : -0.468003 
boosting RMSE 81 : 0.1342745 

forest 81 : -0.3284329 
forest mean 81 : -0.3855836 
forest RMSE 81 : 0.05483039 

nnet 81 : -0.426293 
nnet mean 81 : -0.3904641 
nnet RMSE 81 : 0.1395951 


s: 82 
logit 82 : -0.4905839 
logit mean 82 : -0.4388574 
logit RMSE 82 : 0.06885466 

boosting 82 : -0.552084 
boosting mean 82 : -0.4690284 
boosting RMSE 82 : 0.1345059 

forest 82 : -0.5126498 
forest mean 82 : -0.3871332 
forest RMSE 82 : 0.05589691 

nnet 82 : -0.2648297 
nnet mean 82 : -0.388932 
nnet RMSE 82 : 0.1395420 


s: 83 
logit 83 : -0.4506061 
logit mean 83 : -0.4389989 
logit RMSE 83 : 0.06866367 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 83 : -0.45928 
boosting mean 83 : -0.4689110 
boosting RMSE 83 : 0.1338514 

forest 83 : -0.3826223 
forest mean 83 : -0.3870789 
forest RMSE 83 : 0.05559189 

nnet 83 : -0.5399096 
nnet mean 83 : -0.390751 
nnet RMSE 83 : 0.1395464 


s: 84 
logit 84 : -0.4005938 
logit mean 84 : -0.4385417 
logit RMSE 84 : 0.06825376 

boosting 84 : -0.4390561 
boosting mean 84 : -0.4685555 
boosting RMSE 84 : 0.1331205 

forest 84 : -0.3889259 
forest mean 84 : -0.3871008 
forest RMSE 84 : 0.0552732 

nnet 84 : -0.3829488 
nnet mean 84 : -0.3906581 
nnet RMSE 84 : 0.1387258 


s: 85 
logit 85 : -0.4154083 
logit mean 85 : -0.4382696 
logit RMSE 85 : 0.06787166 

boosting 85 : -0.5229467 
boosting mean 85 : -0.4691954 
boosting RMSE 85 : 0.1330053 

forest 85 : -0.4449283 
forest mean 85 : -0.3877812 
forest RMSE 85 : 0.05516278 

nnet 85 : -0.4791726 
nnet mean 85 : -0.3916994 
nnet RMSE 85 : 0.1381744 


s: 86 
logit 86 : -0.3815664 
logit mean 86 : -0.4376102 
logit RMSE 86 : 0.06750517 

boosting 86 : -0.4498061 
boosting mean 86 : -0.46897 
boosting RMSE 86 : 0.1323388 

forest 86 : -0.3745347 
forest mean 86 : -0.3876271 
forest RMSE 86 : 0.05490983 

nnet 86 : -0.4382357 
nnet mean 86 : -0.3922406 
nnet RMSE 86 : 0.1374306 


s: 87 
logit 87 : -0.2754347 
logit mean 87 : -0.4357461 
logit RMSE 87 : 0.06843187 

boosting 87 : -0.2910415 
boosting mean 87 : -0.4669248 
boosting RMSE 87 : 0.1320936 

forest 87 : -0.3546538 
forest mean 87 : -0.3872481 
forest RMSE 87 : 0.05480939 

nnet 87 : -0.3770732 
nnet mean 87 : -0.3920662 
nnet RMSE 87 : 0.1366606 


s: 88 
logit 88 : -0.4242094 
logit mean 88 : -0.435615 
logit RMSE 88 : 0.06809086 

boosting 88 : -0.5004744 
boosting mean 88 : -0.4673061 
boosting RMSE 88 : 0.1317769 

forest 88 : -0.3621305 
forest mean 88 : -0.3869627 
forest RMSE 88 : 0.05464639 

nnet 88 : -0.4691654 
nnet mean 88 : -0.3929424 
nnet RMSE 88 : 0.1360818 


s: 89 
logit 89 : -0.5023328 
logit mean 89 : -0.4363647 
logit RMSE 89 : 0.06857065 

boosting 89 : -0.527849 
boosting mean 89 : -0.4679863 
boosting RMSE 89 : 0.1317334 

forest 89 : -0.4023092 
forest mean 89 : -0.3871351 
forest RMSE 89 : 0.05433907 

nnet 89 : -0.3246998 
nnet mean 89 : -0.3921756 
nnet RMSE 89 : 0.1355503 


s: 90 
logit 90 : -0.4485793 
logit mean 90 : -0.4365004 
logit RMSE 90 : 0.06838064 

boosting 90 : -0.4566908 
boosting mean 90 : -0.4678608 
boosting RMSE 90 : 0.1311357 

forest 90 : -0.363545 
forest mean 90 : -0.386873 
forest RMSE 90 : 0.05417281 

nnet 90 : -0.5428182 
nnet mean 90 : -0.3938494 
nnet RMSE 90 : 0.1356332 


s: 91 
logit 91 : -0.48038 
logit mean 91 : -0.4369826 
logit RMSE 91 : 0.06852392 

boosting 91 : -0.4300346 
boosting mean 91 : -0.4674451 
boosting RMSE 91 : 0.1304512 

forest 91 : -0.3859662 
forest mean 91 : -0.3868631 
forest RMSE 91 : 0.05389442 

nnet 91 : -0.3358025 
nnet mean 91 : -0.3932115 
nnet RMSE 91 : 0.1350537 


s: 92 
logit 92 : -0.5887272 
logit mean 92 : -0.438632 
logit RMSE 92 : 0.07093406 

boosting 92 : -0.5401201 
boosting mean 92 : -0.4682351 
boosting RMSE 92 : 0.1305601 

forest 92 : -0.385833 
forest mean 92 : -0.3868519 
forest RMSE 92 : 0.05362106 

nnet 92 : -0.3903688 
nnet mean 92 : -0.3931806 
nnet RMSE 92 : 0.1343215 


s: 93 
logit 93 : -0.5986276 
logit mean 93 : -0.4403524 
logit RMSE 93 : 0.07349669 

boosting 93 : -0.7101917 
boosting mean 93 : -0.4708368 
boosting RMSE 93 : 0.1337807 

forest 93 : -0.3564127 
forest mean 93 : -0.3865246 
forest RMSE 93 : 0.05352317 

nnet 93 : -0.4715126 
nnet mean 93 : -0.3940229 
nnet RMSE 93 : 0.133803 


s: 94 
logit 94 : -0.4257117 
logit mean 94 : -0.4401966 
logit RMSE 94 : 0.07315279 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 94 : -0.5775368 
boosting mean 94 : -0.4719719 
boosting RMSE 94 : 0.1343212 

forest 94 : -0.4579911 
forest mean 94 : -0.3872848 
forest RMSE 94 : 0.05357266 

nnet 94 : -0.1741142 
nnet mean 94 : -0.3916834 
nnet RMSE 94 : 0.1351133 


s: 95 
logit 95 : -0.4309907 
logit mean 95 : -0.4400997 
logit RMSE 95 : 0.07283619 

boosting 95 : -0.5285485 
boosting mean 95 : -0.4725674 
boosting RMSE 95 : 0.1342617 

forest 95 : -0.3942404 
forest mean 95 : -0.3873581 
forest RMSE 95 : 0.05329323 

nnet 95 : -0.2913349 
nnet mean 95 : -0.3906271 
nnet RMSE 95 : 0.1348619 


s: 96 
logit 96 : -0.4858464 
logit mean 96 : -0.4405762 
logit RMSE 96 : 0.07298366 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 96 : -0.3988642 
boosting mean 96 : -0.4717997 
boosting RMSE 96 : 0.1335607 

forest 96 : -0.3666404 
forest mean 96 : -0.3871423 
forest RMSE 96 : 0.05312416 

nnet 96 : -0.2398671 
nnet mean 96 : -0.3890567 
nnet RMSE 96 : 0.1351495 


s: 97 
logit 97 : -0.4899531 
logit mean 97 : -0.4410853 
logit RMSE 97 : 0.07317868 

boosting 97 : -0.8391292 
boosting mean 97 : -0.4755866 
boosting RMSE 97 : 0.1401518 

forest 97 : -0.3580183 
forest mean 97 : -0.386842 
forest RMSE 97 : 0.05302123 

nnet 97 : -0.487418 
nnet mean 97 : -0.3900708 
nnet RMSE 97 : 0.1347437 


s: 98 
logit 98 : -0.5500453 
logit mean 98 : -0.4421971 
logit RMSE 98 : 0.07436536 

boosting 98 : -0.7225298 
boosting mean 98 : -0.4781064 
boosting RMSE 98 : 0.1431907 

forest 98 : -0.4948746 
forest mean 98 : -0.3879444 
forest RMSE 98 : 0.05361356 

nnet 98 : -0.4035564 
nnet mean 98 : -0.3902084 
nnet RMSE 98 : 0.1340549 


s: 99 
logit 99 : -0.3779755 
logit mean 99 : -0.4415484 
logit RMSE 99 : 0.07402192 

boosting 99 : -0.3689833 
boosting mean 99 : -0.4770042 
boosting RMSE 99 : 0.1424998 

forest 99 : -0.3857834 
forest mean 99 : -0.3879225 
forest RMSE 99 : 0.05336123 

nnet 99 : -0.4219443 
nnet mean 99 : -0.3905289 
nnet RMSE 99 : 0.1333944 


s: 100 
logit 100 : -0.4790649 
logit mean 100 : -0.4419236 
logit RMSE 100 : 0.07407405 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 100 : -0.4377943 
boosting mean 100 : -0.4766121 
boosting RMSE 100 : 0.1418359 

forest 100 : -0.3954325 
forest mean 100 : -0.3879976 
forest RMSE 100 : 0.05309572 

nnet 100 : -0.2896483 
nnet mean 100 : -0.3895201 
nnet RMSE 100 : 0.1331837 


s: 101 
logit 101 : -0.4327933 
logit mean 101 : -0.4418332 
logit RMSE 101 : 0.07377863 

boosting 101 : -0.6168971 
boosting mean 101 : -0.478001 
boosting RMSE 101 : 0.1427726 

forest 101 : -0.3062178 
forest mean 101 : -0.3871879 
forest RMSE 101 : 0.05365 

nnet 101 : -0.5472363 
nnet mean 101 : -0.3910817 
nnet RMSE 101 : 0.1333301 


s: 102 
logit 102 : -0.5485772 
logit mean 102 : -0.4428797 
logit RMSE 102 : 0.07487553 

boosting 102 : -0.6369456 
boosting mean 102 : -0.4795593 
boosting RMSE 102 : 0.1439952 

forest 102 : -0.411271 
forest mean 102 : -0.3874241 
forest RMSE 102 : 0.05339803 

nnet 102 : -0.3755461 
nnet mean 102 : -0.3909294 
nnet RMSE 102 : 0.1326970 


s: 103 
logit 103 : -0.4567101 
logit mean 103 : -0.443014 
logit RMSE 103 : 0.0747204 

boosting 103 : -0.67814 
boosting mean 103 : -0.4814873 
boosting RMSE 103 : 0.1458917 

forest 103 : -0.4248577 
forest mean 103 : -0.3877875 
forest RMSE 103 : 0.0531946 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 103 : -0.2078486 
nnet mean 103 : -0.3891519 
nnet RMSE 103 : 0.1334017 


s: 104 
logit 104 : -0.4812231 
logit mean 104 : -0.4433814 
logit RMSE 104 : 0.07478561 

boosting 104 : -0.5056935 
boosting mean 104 : -0.48172 
boosting RMSE 104 : 0.1455580 

forest 104 : -0.532452 
forest mean 104 : -0.3891785 
forest RMSE 104 : 0.05450821 

nnet 104 : -0.535931 
nnet mean 104 : -0.3905632 
nnet RMSE 104 : 0.1334262 


s: 105 
logit 105 : -0.3904583 
logit mean 105 : -0.4428773 
logit RMSE 105 : 0.07443447 

boosting 105 : -0.4330162 
boosting mean 105 : -0.4812562 
boosting RMSE 105 : 0.1448991 

forest 105 : -0.3848668 
forest mean 105 : -0.3891374 
forest RMSE 105 : 0.05426813 

nnet 105 : -0.3327207 
nnet mean 105 : -0.3900123 
nnet RMSE 105 : 0.1329516 


s: 106 
logit 106 : -0.3341435 
logit mean 106 : -0.4418515 
logit RMSE 106 : 0.07435816 

boosting 106 : -0.6063495 
boosting mean 106 : -0.4824363 
boosting RMSE 106 : 0.1456000 

forest 106 : -0.4348371 
forest mean 106 : -0.3895686 
forest RMSE 106 : 0.05411742 

nnet 106 : -0.6704669 
nnet mean 106 : -0.3926581 
nnet RMSE 106 : 0.1349055 


s: 107 
logit 107 : -0.4560526 
logit mean 107 : -0.4419843 
logit RMSE 107 : 0.07420799 

boosting 107 : -0.4579331 
boosting mean 107 : -0.4822073 
boosting RMSE 107 : 0.1450262 

forest 107 : -0.4104178 
forest mean 107 : -0.3897634 
forest RMSE 107 : 0.05387336 

nnet 107 : -0.3544248 
nnet mean 107 : -0.3923008 
nnet RMSE 107 : 0.1343458 


s: 108 
logit 108 : -0.369821 
logit mean 108 : -0.4413161 
logit RMSE 108 : 0.0739207 

boosting 108 : -0.4801049 
boosting mean 108 : -0.4821878 
boosting RMSE 108 : 0.1445589 

forest 108 : -0.4688702 
forest mean 108 : -0.3904959 
forest RMSE 108 : 0.05403132 

nnet 108 : -0.3110103 
nnet mean 108 : -0.3915481 
nnet RMSE 108 : 0.1339963 


s: 109 
logit 109 : -0.3700192 
logit mean 109 : -0.440662 
logit RMSE 109 : 0.07363685 

boosting 109 : -0.3610045 
boosting mean 109 : -0.4810761 
boosting RMSE 109 : 0.1439427 

forest 109 : -0.3919739 
forest mean 109 : -0.3905094 
forest RMSE 109 : 0.05378839 

nnet 109 : -0.4958392 
nnet mean 109 : -0.3925049 
nnet RMSE 109 : 0.1336958 


s: 110 
logit 110 : -0.4180306 
logit mean 110 : -0.4404562 
logit RMSE 110 : 0.07332153 

boosting 110 : -0.5647847 
boosting mean 110 : -0.481837 
boosting RMSE 110 : 0.1441458 

forest 110 : -0.4020029 
forest mean 110 : -0.3906139 
forest RMSE 110 : 0.05354368 

nnet 110 : -0.3296250 
nnet mean 110 : -0.3919333 
nnet RMSE 110 : 0.1332557 


s: 111 
logit 111 : -0.4942009 
logit mean 111 : -0.4409404 
logit RMSE 111 : 0.0735361 

boosting 111 : -0.4174023 
boosting mean 111 : -0.4812566 
boosting RMSE 111 : 0.1435045 

forest 111 : -0.344413 
forest mean 111 : -0.3901977 
forest RMSE 111 : 0.05356243 

nnet 111 : -0.4070384 
nnet mean 111 : -0.3920694 
nnet RMSE 111 : 0.1326558 


s: 112 
logit 112 : -0.4363926 
logit mean 112 : -0.4408998 
logit RMSE 112 : 0.0732878 

boosting 112 : -0.5272546 
boosting mean 112 : -0.4816672 
boosting RMSE 112 : 0.1433676 

forest 112 : -0.3906438 
forest mean 112 : -0.3902017 
forest RMSE 112 : 0.05333011 

nnet 112 : -0.4020021 
nnet mean 112 : -0.3921581 
nnet RMSE 112 : 0.1320624 


s: 113 
logit 113 : -0.4618581 
logit mean 113 : -0.4410853 
logit RMSE 113 : 0.07319448 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 113 : -0.4812464 
boosting mean 113 : -0.4816635 
boosting RMSE 113 : 0.1429363 

forest 113 : -0.4258134 
forest mean 113 : -0.3905168 
forest RMSE 113 : 0.05314911 

nnet 113 : -0.2724644 
nnet mean 113 : -0.3910988 
nnet RMSE 113 : 0.132023 


s: 114 
logit 114 : -0.4273803 
logit mean 114 : -0.4409651 
logit RMSE 114 : 0.07291785 

boosting 114 : -0.5502561 
boosting mean 114 : -0.4822652 
boosting RMSE 114 : 0.1430021 

forest 114 : -0.371143 
forest mean 114 : -0.3903469 
forest RMSE 114 : 0.05298447 

nnet 114 : -0.2639865 
nnet mean 114 : -0.3899838 
nnet RMSE 114 : 0.1320585 


s: 115 
logit 115 : -0.439271 
logit mean 115 : -0.4409503 
logit RMSE 115 : 0.07269242 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 115 : -0.5487641 
boosting mean 115 : -0.4828435 
boosting RMSE 115 : 0.1430532 

forest 115 : -0.4129883 
forest mean 115 : -0.3905438 
forest RMSE 115 : 0.0527675 

nnet 115 : -0.4622459 
nnet mean 115 : -0.3906122 
nnet RMSE 115 : 0.1316112 


s: 116 
logit 116 : -0.5139418 
logit mean 116 : -0.4415796 
logit RMSE 116 : 0.07314749 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 116 : -0.5087773 
boosting mean 116 : -0.483067 
boosting RMSE 116 : 0.1427929 

forest 116 : -0.3676991 
forest mean 116 : -0.3903468 
forest RMSE 116 : 0.05262509 

nnet 116 : -0.3946455 
nnet mean 116 : -0.3906469 
nnet RMSE 116 : 0.1310436 


s: 117 
logit 117 : -0.4660839 
logit mean 117 : -0.441789 
logit RMSE 117 : 0.07309001 

boosting 117 : -0.4969603 
boosting mean 117 : -0.4831858 
boosting RMSE 117 : 0.1424637 

forest 117 : -0.3884312 
forest mean 117 : -0.3903305 
forest RMSE 117 : 0.05241062 

nnet 117 : -0.2835803 
nnet mean 117 : -0.3897318 
nnet RMSE 117 : 0.1309255 


s: 118 
logit 118 : -0.4363405 
logit mean 118 : -0.4417428 
logit RMSE 118 : 0.0728565 

boosting 118 : -0.336102 
boosting mean 118 : -0.4819393 
boosting RMSE 118 : 0.1419806 

forest 118 : -0.3817878 
forest mean 118 : -0.3902581 
forest RMSE 118 : 0.052215 

nnet 118 : -0.2186701 
nnet mean 118 : -0.3882822 
nnet RMSE 118 : 0.1314339 


s: 119 
logit 119 : -0.4514098 
logit mean 119 : -0.4418241 
logit RMSE 119 : 0.07270263 

boosting 119 : -0.4360909 
boosting mean 119 : -0.481554 
boosting RMSE 119 : 0.1414215 

forest 119 : -0.4335227 
forest mean 119 : -0.3906216 
forest RMSE 119 : 0.05208587 

nnet 119 : -0.4171029 
nnet mean 119 : -0.3885244 
nnet RMSE 119 : 0.1308899 


s: 120 
logit 120 : -0.4776089 
logit mean 120 : -0.4421223 
logit RMSE 120 : 0.07274489 

boosting 120 : -0.4229045 
boosting mean 120 : -0.4810653 
boosting RMSE 120 : 0.1408465 

forest 120 : -0.4192313 
forest mean 120 : -0.3908600 
forest RMSE 120 : 0.0518981 

nnet 120 : -0.5262635 
nnet mean 120 : -0.3896722 
nnet RMSE 120 : 0.130852 


s: 121 
logit 121 : -0.3735747 
logit mean 121 : -0.4415558 
logit RMSE 121 : 0.07248348 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 121 : -0.4519035 
boosting mean 121 : -0.4808243 
boosting RMSE 121 : 0.1403427 

forest 121 : -0.4300597 
forest mean 121 : -0.391184 
forest RMSE 121 : 0.05175539 

nnet 121 : -0.03570031 
nnet mean 121 : -0.3867468 
nnet RMSE 121 : 0.1344528 


s: 122 
logit 122 : -0.4281587 
logit mean 122 : -0.441446 
logit RMSE 122 : 0.07223081 

boosting 122 : -0.4556584 
boosting mean 122 : -0.480618 
boosting RMSE 122 : 0.1398571 

forest 122 : -0.4188958 
forest mean 122 : -0.3914112 
forest RMSE 122 : 0.05157122 

nnet 122 : -0.3570775 
nnet mean 122 : -0.3865036 
nnet RMSE 122 : 0.133957 


s: 123 
logit 123 : -0.4231705 
logit mean 123 : -0.4412974 
logit RMSE 123 : 0.07196692 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 123 : -0.4194207 
boosting mean 123 : -0.4801205 
boosting RMSE 123 : 0.1392984 

forest 123 : -0.4386212 
forest mean 123 : -0.391795 
forest RMSE 123 : 0.05147908 

nnet 123 : -0.6612832 
nnet mean 123 : -0.3887376 
nnet RMSE 123 : 0.1354755 


s: 124 
logit 124 : -0.4665893 
logit mean 124 : -0.4415014 
logit RMSE 124 : 0.07192516 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 124 : -0.5977758 
boosting mean 124 : -0.4810693 
boosting RMSE 124 : 0.1398679 

forest 124 : -0.3961848 
forest mean 124 : -0.3918304 
forest RMSE 124 : 0.05127222 

nnet 124 : -0.1673057 
nnet mean 124 : -0.3869518 
nnet RMSE 124 : 0.1365367 


s: 125 
logit 125 : -0.4005627 
logit mean 125 : -0.4411738 
logit RMSE 125 : 0.0716369 

boosting 125 : -0.4559472 
boosting mean 125 : -0.4808683 
boosting RMSE 125 : 0.1393971 

forest 125 : -0.3810539 
forest mean 125 : -0.3917442 
forest RMSE 125 : 0.05109483 

nnet 125 : -0.3359706 
nnet mean 125 : -0.386544 
nnet RMSE 125 : 0.13611 


s: 126 
logit 126 : -0.5346182 
logit mean 126 : -0.4419155 
logit RMSE 126 : 0.0723529 

boosting 126 : -0.4256194 
boosting mean 126 : -0.4804298 
boosting RMSE 126 : 0.1388616 

forest 126 : -0.4391234 
forest mean 126 : -0.3921202 
forest RMSE 126 : 0.05101088 

nnet 126 : -0.4236308 
nnet mean 126 : -0.3868383 
nnet RMSE 126 : 0.1355852 


s: 127 
logit 127 : -0.3786682 
logit mean 127 : -0.4414175 
logit RMSE 127 : 0.07209234 

boosting 127 : -0.4640851 
boosting mean 127 : -0.4803011 
boosting RMSE 127 : 0.1384307 

forest 127 : -0.3868491 
forest mean 127 : -0.3920787 
forest RMSE 127 : 0.05082305 

nnet 127 : -0.3682811 
nnet mean 127 : -0.3866922 
nnet RMSE 127 : 0.1350796 


s: 128 
logit 128 : -0.4283236 
logit mean 128 : -0.4413152 
logit RMSE 128 : 0.0718538 

boosting 128 : -0.6388468 
boosting mean 128 : -0.4815398 
boosting RMSE 128 : 0.1394956 

forest 128 : -0.3462066 
forest mean 128 : -0.3917203 
forest RMSE 128 : 0.05084693 

nnet 128 : -0.6497124 
nnet mean 128 : -0.3887471 
nnet RMSE 128 : 0.1363492 


s: 129 
logit 129 : -0.4692929 
logit mean 129 : -0.441532 
logit RMSE 129 : 0.0718343 

boosting 129 : -0.5003989 
boosting mean 129 : -0.481686 
boosting RMSE 129 : 0.1392348 

forest 129 : -0.4150675 
forest mean 129 : -0.3919013 
forest RMSE 129 : 0.05066684 

nnet 129 : -0.6072497 
nnet mean 129 : -0.3904409 
nnet RMSE 129 : 0.13704 


s: 130 
logit 130 : -0.4702598 
logit mean 130 : -0.441753 
logit RMSE 130 : 0.07182232 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 130 : -0.3702609 
boosting mean 130 : -0.4808288 
boosting RMSE 130 : 0.1387227 

forest 130 : -0.3662805 
forest mean 130 : -0.3917042 
forest RMSE 130 : 0.05055816 

nnet 130 : -0.3147742 
nnet mean 130 : -0.3898588 
nnet RMSE 130 : 0.1367164 


s: 131 
logit 131 : -0.4308774 
logit mean 131 : -0.44167 
logit RMSE 131 : 0.07159851 

boosting 131 : -0.5157465 
boosting mean 131 : -0.4810954 
boosting RMSE 131 : 0.1385618 

forest 131 : -0.4429598 
forest mean 131 : -0.3920955 
forest RMSE 131 : 0.05050449 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 131 : -0.2901171 
nnet mean 131 : -0.3890974 
nnet RMSE 131 : 0.1365315 


s: 132 
logit 132 : -0.3758522 
logit mean 132 : -0.4411714 
logit RMSE 132 : 0.07135775 

boosting 132 : -0.328336 
boosting mean 132 : -0.4799381 
boosting RMSE 132 : 0.1381768 

forest 132 : -0.388041 
forest mean 132 : -0.3920648 
forest RMSE 132 : 0.05032359 

nnet 132 : 0.1319151 
nnet mean 132 : -0.3851504 
nnet RMSE 132 : 0.143677 


s: 133 
logit 133 : -0.3231916 
logit mean 133 : -0.4402843 
logit RMSE 133 : 0.07140028 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 133 : -0.431121 
boosting mean 133 : -0.4795711 
boosting RMSE 133 : 0.1376828 

forest 133 : -0.3847499 
forest mean 133 : -0.3920098 
forest RMSE 133 : 0.05015148 

nnet 133 : -0.2845308 
nnet mean 133 : -0.3843938 
nnet RMSE 133 : 0.1434856 


s: 134 
logit 134 : -0.4663808 
logit mean 134 : -0.4404791 
logit RMSE 134 : 0.07136413 

boosting 134 : -0.5521002 
boosting mean 134 : -0.4801123 
boosting RMSE 134 : 0.1377960 

forest 134 : -0.3899731 
forest mean 134 : -0.3919946 
forest RMSE 134 : 0.0499715 

nnet 134 : -0.2249922 
nnet mean 134 : -0.3832043 
nnet RMSE 134 : 0.1437464 


s: 135 
logit 135 : -0.3699662 
logit mean 135 : -0.4399568 
logit RMSE 135 : 0.0711463 

boosting 135 : -0.3755245 
boosting mean 135 : -0.4793376 
boosting RMSE 135 : 0.1373008 

forest 135 : -0.4230485 
forest mean 135 : -0.3922246 
forest RMSE 135 : 0.04982558 

nnet 135 : -0.4598226 
nnet mean 135 : -0.3837718 
nnet RMSE 135 : 0.1433056 


s: 136 
logit 136 : -0.4456471 
logit mean 136 : -0.4399986 
logit RMSE 136 : 0.07099224 

boosting 136 : -0.3977433 
boosting mean 136 : -0.4787377 
boosting RMSE 136 : 0.1367952 

forest 136 : -0.4006108 
forest mean 136 : -0.3922863 
forest RMSE 136 : 0.04964209 

nnet 136 : -0.3453271 
nnet mean 136 : -0.3834891 
nnet RMSE 136 : 0.1428547 


s: 137 
logit 137 : -0.3601488 
logit mean 137 : -0.4394157 
logit RMSE 137 : 0.07081457 

boosting 137 : -0.3068156 
boosting mean 137 : -0.4774828 
boosting RMSE 137 : 0.1365274 

forest 137 : -0.304403 
forest mean 137 : -0.3916448 
forest RMSE 137 : 0.05013039 

nnet 137 : -0.4548359 
nnet mean 137 : -0.3840099 
nnet RMSE 137 : 0.1424095 


s: 138 
logit 138 : -0.3329804 
logit mean 138 : -0.4386445 
logit RMSE 138 : 0.0707878 

boosting 138 : -0.4198554 
boosting mean 138 : -0.4770652 
boosting RMSE 138 : 0.1360423 

forest 138 : -0.4392808 
forest mean 138 : -0.39199 
forest RMSE 138 : 0.05006023 

nnet 138 : -0.3757113 
nnet mean 138 : -0.3839498 
nnet RMSE 138 : 0.1419076 


s: 139 
logit 139 : -0.3906175 
logit mean 139 : -0.438299 
logit RMSE 139 : 0.0705372 

boosting 139 : -0.5840392 
boosting mean 139 : -0.4778348 
boosting RMSE 139 : 0.1364479 

forest 139 : -0.4136677 
forest mean 139 : -0.3921459 
forest RMSE 139 : 0.0498933 

nnet 139 : -0.3608026 
nnet mean 139 : -0.3837832 
nnet RMSE 139 : 0.1414353 


s: 140 
logit 140 : -0.3872587 
logit mean 140 : -0.4379344 
logit RMSE 140 : 0.07029307 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 140 : -0.4509164 
boosting mean 140 : -0.4776425 
boosting RMSE 140 : 0.1360278 

forest 140 : -0.3825366 
forest mean 140 : -0.3920773 
forest RMSE 140 : 0.04973669 

nnet 140 : -0.259773 
nnet mean 140 : -0.3828975 
nnet RMSE 140 : 0.1414267 


s: 141 
logit 141 : -0.4484359 
logit mean 141 : -0.4380089 
logit RMSE 141 : 0.07016204 

boosting 141 : -0.425668 
boosting mean 141 : -0.4772739 
boosting RMSE 141 : 0.1355618 

forest 141 : -0.3091644 
forest mean 141 : -0.3914892 
forest RMSE 141 : 0.05014691 

nnet 141 : -0.4552564 
nnet mean 141 : -0.3834106 
nnet RMSE 141 : 0.1410011 


s: 142 
logit 142 : -0.4859993 
logit mean 142 : -0.4383468 
logit RMSE 142 : 0.07028605 

boosting 142 : -0.5014557 
boosting mean 142 : -0.4774442 
boosting RMSE 142 : 0.1353517 

forest 142 : -0.442232 
forest mean 142 : -0.3918466 
forest RMSE 142 : 0.05009555 

nnet 142 : -0.4614091 
nnet mean 142 : -0.3839599 
nnet RMSE 142 : 0.1405982 


s: 143 
logit 143 : -0.5219962 
logit mean 143 : -0.4389318 
logit RMSE 143 : 0.07077895 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 143 : -0.8562451 
boosting mean 143 : -0.4800931 
boosting RMSE 143 : 0.1401700 

forest 143 : -0.3839638 
forest mean 143 : -0.3917915 
forest RMSE 143 : 0.04993809 

nnet 143 : -0.606752 
nnet mean 143 : -0.3855179 
nnet RMSE 143 : 0.1411685 


s: 144 
logit 144 : -0.4438157 
logit mean 144 : -0.4389657 
logit RMSE 144 : 0.0706272 

boosting 144 : -0.5965422 
boosting mean 144 : -0.4809018 
boosting RMSE 144 : 0.1406394 

forest 144 : -0.4046223 
forest mean 144 : -0.3918806 
forest RMSE 144 : 0.04976588 

nnet 144 : -0.607587 
nnet mean 144 : -0.3870601 
nnet RMSE 144 : 0.1417371 


s: 145 
logit 145 : -0.4390985 
logit mean 145 : -0.4389666 
logit RMSE 145 : 0.0704581 

boosting 145 : -0.5254553 
boosting mean 145 : -0.4812091 
boosting RMSE 145 : 0.1405403 

forest 145 : -0.4143215 
forest mean 145 : -0.3920353 
forest RMSE 145 : 0.04960824 

nnet 145 : -0.4748908 
nnet mean 145 : -0.3876658 
nnet RMSE 145 : 0.1413844 


s: 146 
logit 146 : -0.3989824 
logit mean 146 : -0.4386928 
logit RMSE 146 : 0.07021644 

boosting 146 : -0.4557595 
boosting mean 146 : -0.4810348 
boosting RMSE 146 : 0.1401342 

forest 146 : -0.2838101 
forest mean 146 : -0.3912941 
forest RMSE 146 : 0.05036455 

nnet 146 : -0.4515867 
nnet mean 146 : -0.3881036 
nnet RMSE 146 : 0.1409640 


s: 147 
logit 147 : -0.3498199 
logit mean 147 : -0.4380882 
logit RMSE 147 : 0.07009949 

boosting 147 : -0.5667899 
boosting mean 147 : -0.4816181 
boosting RMSE 147 : 0.1403326 

forest 147 : -0.3950862 
forest mean 147 : -0.3913199 
forest RMSE 147 : 0.05019458 

nnet 147 : -0.3876495 
nnet mean 147 : -0.3881005 
nnet RMSE 147 : 0.1404874 


s: 148 
logit 148 : -0.4879241 
logit mean 148 : -0.4384249 
logit RMSE 148 : 0.0702351 

boosting 148 : -0.455259 
boosting mean 148 : -0.48144 
boosting RMSE 148 : 0.1399315 

forest 148 : -0.3884997 
forest mean 148 : -0.3913008 
forest RMSE 148 : 0.05003365 

nnet 148 : -0.4510488 
nnet mean 148 : -0.3885258 
nnet RMSE 148 : 0.1400749 


s: 149 
logit 149 : -0.5017417 
logit mean 149 : -0.4388499 
logit RMSE 149 : 0.07049351 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 149 : -0.5450446 
boosting mean 149 : -0.4818669 
boosting RMSE 149 : 0.1399664 

forest 149 : -0.4135552 
forest mean 149 : -0.3914502 
forest RMSE 149 : 0.04987783 

nnet 149 : -0.2796423 
nnet mean 149 : -0.3877951 
nnet RMSE 149 : 0.1399518 


s: 150 
logit 150 : -0.4823364 
logit mean 150 : -0.4391398 
logit RMSE 150 : 0.07057904 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 150 : -0.5074576 
boosting mean 150 : -0.4820375 
boosting RMSE 150 : 0.1397747 

forest 150 : -0.4195156 
forest mean 150 : -0.3916373 
forest RMSE 150 : 0.04973683 

nnet 150 : -0.4035437 
nnet mean 150 : -0.3879001 
nnet RMSE 150 : 0.1394848 


s: 151 
logit 151 : -0.4184854 
logit mean 151 : -0.439003 
logit RMSE 151 : 0.07036103 

boosting 151 : -0.3585957 
boosting mean 151 : -0.48122 
boosting RMSE 151 : 0.1393519 

forest 151 : -0.3477118 
forest mean 151 : -0.3913464 
forest RMSE 151 : 0.04975415 

nnet 151 : -0.4896013 
nnet mean 151 : -0.3885736 
nnet RMSE 151 : 0.1392133 


s: 152 
logit 152 : -0.356018 
logit mean 152 : -0.438457 
logit RMSE 152 : 0.07021988 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 152 : -0.3395964 
boosting mean 152 : -0.4802883 
boosting RMSE 152 : 0.1389791 

forest 152 : -0.3524216 
forest mean 152 : -0.3910903 
forest RMSE 152 : 0.04974015 

nnet 152 : -0.5503971 
nnet mean 152 : -0.3896382 
nnet RMSE 152 : 0.1392898 


s: 153 
logit 153 : -0.3991273 
logit mean 153 : -0.4382 
logit RMSE 153 : 0.06999006 

boosting 153 : -0.5589762 
boosting mean 153 : -0.4808026 
boosting RMSE 153 : 0.1391191 

forest 153 : -0.3374117 
forest mean 153 : -0.3907394 
forest RMSE 153 : 0.04983488 

nnet 153 : -0.5715174 
nnet mean 153 : -0.390827 
nnet RMSE 153 : 0.1395246 


s: 154 
logit 154 : -0.434704 
logit mean 154 : -0.4381773 
logit RMSE 154 : 0.06981848 

boosting 154 : -0.4530719 
boosting mean 154 : -0.4806225 
boosting RMSE 154 : 0.1387327 

forest 154 : -0.4670984 
forest mean 154 : -0.3912353 
forest RMSE 154 : 0.04996622 

nnet 154 : -0.5736411 
nnet mean 154 : -0.3920141 
nnet RMSE 154 : 0.139773 


s: 155 
logit 155 : -0.4082448 
logit mean 155 : -0.4379842 
logit RMSE 155 : 0.06959604 

boosting 155 : -0.5741563 
boosting mean 155 : -0.4812259 
boosting RMSE 155 : 0.1389901 

forest 155 : -0.3827904 
forest mean 155 : -0.3911808 
forest RMSE 155 : 0.04982396 

nnet 155 : -0.07424587 
nnet mean 155 : -0.3899640 
nnet RMSE 155 : 0.1417571 


s: 156 
logit 156 : -0.4445803 
logit mean 156 : -0.4380264 
logit RMSE 156 : 0.06946438 

boosting 156 : -0.2809854 
boosting mean 156 : -0.4799424 
boosting RMSE 156 : 0.1388712 

forest 156 : -0.3738624 
forest mean 156 : -0.3910698 
forest RMSE 156 : 0.04970808 

nnet 156 : -0.6501792 
nnet mean 156 : -0.391632 
nnet RMSE 156 : 0.1427146 


s: 157 
logit 157 : -0.4112172 
logit mean 157 : -0.4378557 
logit RMSE 157 : 0.06924859 

boosting 157 : -0.5769063 
boosting mean 157 : -0.48056 
boosting RMSE 157 : 0.1391464 

forest 157 : -0.3517731 
forest mean 157 : -0.3908195 
forest RMSE 157 : 0.04969879 

nnet 157 : -0.5887831 
nnet mean 157 : -0.3928877 
nnet RMSE 157 : 0.143055 


s: 158 
logit 158 : -0.4533793 
logit mean 158 : -0.4379539 
logit RMSE 158 : 0.0691596 

boosting 158 : -0.4418503 
boosting mean 158 : -0.480315 
boosting RMSE 158 : 0.1387453 

forest 158 : -0.4456109 
forest mean 158 : -0.3911663 
forest RMSE 158 : 0.04967397 

nnet 158 : -0.7543826 
nnet mean 158 : -0.3951757 
nnet RMSE 158 : 0.1453619 


s: 159 
logit 159 : -0.4617151 
logit mean 159 : -0.4381034 
logit RMSE 159 : 0.06911529 

boosting 159 : -0.3709967 
boosting mean 159 : -0.4796274 
boosting RMSE 159 : 0.1383274 

forest 159 : -0.4500256 
forest mean 159 : -0.3915365 
forest RMSE 159 : 0.04967619 

nnet 159 : -0.3918594 
nnet mean 159 : -0.3951548 
nnet RMSE 159 : 0.1449055 


s: 160 
logit 160 : -0.4250086 
logit mean 160 : -0.4380215 
logit RMSE 160 : 0.06892732 

boosting 160 : -0.5260984 
boosting mean 160 : -0.4799179 
boosting RMSE 160 : 0.1382544 

forest 160 : -0.3814561 
forest mean 160 : -0.3914735 
forest RMSE 160 : 0.04954241 

nnet 160 : -0.4880366 
nnet mean 160 : -0.3957353 
nnet RMSE 160 : 0.1446195 


s: 161 
logit 161 : -0.4574956 
logit mean 161 : -0.4381425 
logit RMSE 161 : 0.06886218 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 161 : -0.4625113 
boosting mean 161 : -0.4798098 
boosting RMSE 161 : 0.1379124 

forest 161 : -0.2668344 
forest mean 161 : -0.3906993 
forest RMSE 161 : 0.05049107 

nnet 161 : -0.5891305 
nnet mean 161 : -0.3969366 
nnet RMSE 161 : 0.1449381 


s: 162 
logit 162 : -0.4617046 
logit mean 162 : -0.4382879 
logit RMSE 162 : 0.06882028 

boosting 162 : -0.3752567 
boosting mean 162 : -0.4791644 
boosting RMSE 162 : 0.1374998 

forest 162 : -0.4127056 
forest mean 162 : -0.3908351 
forest RMSE 162 : 0.05034489 

nnet 162 : -0.2772945 
nnet mean 162 : -0.396198 
nnet RMSE 162 : 0.1448114 


s: 163 
logit 163 : -0.4278269 
logit mean 163 : -0.4382238 
logit RMSE 163 : 0.06864346 

boosting 163 : -0.4875203 
boosting mean 163 : -0.4792156 
boosting RMSE 163 : 0.1372487 

forest 163 : -0.4614621 
forest mean 163 : -0.3912684 
forest RMSE 163 : 0.05042057 

nnet 163 : -0.5806857 
nnet mean 163 : -0.3973298 
nnet RMSE 163 : 0.1450585 


s: 164 
logit 164 : -0.4675074 
logit mean 164 : -0.4384023 
logit RMSE 164 : 0.06863659 

boosting 164 : -0.6605972 
boosting mean 164 : -0.4803216 
boosting RMSE 164 : 0.1383345 

forest 164 : -0.3250915 
forest mean 164 : -0.3908649 
forest RMSE 164 : 0.0506058 

nnet 164 : -0.3355029 
nnet mean 164 : -0.3969529 
nnet RMSE 164 : 0.1447033 


s: 165 
logit 165 : -0.4856525 
logit mean 165 : -0.4386887 
logit RMSE 165 : 0.0687524 

boosting 165 : -0.3957269 
boosting mean 165 : -0.4798089 
boosting RMSE 165 : 0.1379150 

forest 165 : -0.3844886 
forest mean 165 : -0.3908263 
forest RMSE 165 : 0.05046667 

nnet 165 : -0.4270773 
nnet mean 165 : -0.3971354 
nnet RMSE 165 : 0.1442795 


s: 166 
logit 166 : -0.4676519 
logit mean 166 : -0.4388632 
logit RMSE 166 : 0.06874582 

boosting 166 : -0.5103472 
boosting mean 166 : -0.4799929 
boosting RMSE 166 : 0.1377655 

forest 166 : -0.43392 
forest mean 166 : -0.3910859 
forest RMSE 166 : 0.05038326 

nnet 166 : -0.2361314 
nnet mean 166 : -0.3961655 
nnet RMSE 166 : 0.1444055 


s: 167 
logit 167 : -0.4357405 
logit mean 167 : -0.4388445 
logit RMSE 167 : 0.06859547 

boosting 167 : -0.584771 
boosting mean 167 : -0.4806203 
boosting RMSE 167 : 0.1380946 

forest 167 : -0.3874171 
forest mean 167 : -0.3910639 
forest RMSE 167 : 0.05024162 

nnet 167 : -0.3106681 
nnet mean 167 : -0.3956536 
nnet RMSE 167 : 0.1441383 


s: 168 
logit 168 : -0.3872482 
logit mean 168 : -0.4385373 
logit RMSE 168 : 0.06839808 

boosting 168 : -0.3859551 
boosting mean 168 : -0.4800568 
boosting RMSE 168 : 0.1376872 

forest 168 : -0.4161366 
forest mean 168 : -0.3912131 
forest RMSE 168 : 0.05010734 

nnet 168 : -0.284184 
nnet mean 168 : -0.3949901 
nnet RMSE 168 : 0.1439862 


s: 169 
logit 169 : -0.5498258 
logit mean 169 : -0.4391958 
logit RMSE 169 : 0.06916244 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 169 : -0.4838941 
boosting mean 169 : -0.4800795 
boosting RMSE 169 : 0.1374309 

forest 169 : -0.3957310 
forest mean 169 : -0.3912399 
forest RMSE 169 : 0.04995995 

nnet 169 : -0.2478770 
nnet mean 169 : -0.3941196 
nnet RMSE 169 : 0.1440357 


s: 170 
logit 170 : -0.3499447 
logit mean 170 : -0.4386708 
logit RMSE 170 : 0.0690655 

boosting 170 : -0.523613 
boosting mean 170 : -0.4803356 
boosting RMSE 170 : 0.1373537 

forest 170 : -0.2780468 
forest mean 170 : -0.390574 
forest RMSE 170 : 0.05068333 

nnet 170 : -0.01534377 
nnet mean 170 : -0.3918915 
nnet RMSE 170 : 0.1466104 


s: 171 
logit 171 : -0.5065329 
logit mean 171 : -0.4390677 
logit RMSE 171 : 0.06934348 

boosting 171 : -0.4701795 
boosting mean 171 : -0.4802762 
boosting RMSE 171 : 0.1370566 

forest 171 : -0.3079481 
forest mean 171 : -0.3900908 
forest RMSE 171 : 0.05102285 

nnet 171 : -0.4729269 
nnet mean 171 : -0.3923654 
nnet RMSE 171 : 0.1462874 


s: 172 
logit 172 : -0.4561494 
logit mean 172 : -0.439167 
logit RMSE 172 : 0.06927403 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 172 : -0.5015753 
boosting mean 172 : -0.4804 
boosting RMSE 172 : 0.1368769 

forest 172 : -0.3529884 
forest mean 172 : -0.3898751 
forest RMSE 172 : 0.05100044 

nnet 172 : -0.5145836 
nnet mean 172 : -0.3930759 
nnet RMSE 172 : 0.1461230 


s: 173 
logit 173 : -0.3804325 
logit mean 173 : -0.4388275 
logit RMSE 173 : 0.06908955 

boosting 173 : -0.397645 
boosting mean 173 : -0.4799217 
boosting RMSE 173 : 0.1364808 

forest 173 : -0.3207389 
forest mean 173 : -0.3894755 
forest RMSE 173 : 0.05120863 

nnet 173 : -0.4738019 
nnet mean 173 : -0.3935426 
nnet RMSE 173 : 0.1458080 


s: 174 
logit 174 : -0.3580944 
logit mean 174 : -0.4383635 
logit RMSE 174 : 0.06896394 

boosting 174 : -0.4247745 
boosting mean 174 : -0.4796047 
boosting RMSE 174 : 0.1361010 

forest 174 : -0.4136276 
forest mean 174 : -0.3896143 
forest RMSE 174 : 0.05107172 

nnet 174 : -0.3575577 
nnet mean 174 : -0.3933357 
nnet RMSE 174 : 0.1454240 


s: 175 
logit 175 : -0.3815512 
logit mean 175 : -0.4380389 
logit RMSE 175 : 0.06878076 

boosting 175 : -0.4207504 
boosting mean 175 : -0.4792684 
boosting RMSE 175 : 0.1357207 

forest 175 : -0.3940676 
forest mean 175 : -0.3896398 
forest RMSE 175 : 0.05092756 

nnet 175 : -0.2866508 
nnet mean 175 : -0.3927261 
nnet RMSE 175 : 0.1452609 


s: 176 
logit 176 : -0.5086753 
logit mean 176 : -0.4384402 
logit RMSE 176 : 0.06907255 

boosting 176 : -0.4311037 
boosting mean 176 : -0.4789948 
boosting RMSE 176 : 0.1353549 

forest 176 : -0.3032862 
forest mean 176 : -0.3891491 
forest RMSE 176 : 0.05130327 

nnet 176 : -0.2497599 
nnet mean 176 : -0.3919138 
nnet RMSE 176 : 0.1452897 


s: 177 
logit 177 : -0.4880752 
logit mean 177 : -0.4387206 
logit RMSE 177 : 0.06919457 

boosting 177 : -0.6224961 
boosting mean 177 : -0.4798055 
boosting RMSE 177 : 0.1360041 

forest 177 : -0.4307984 
forest mean 177 : -0.3893844 
forest RMSE 177 : 0.05121049 

nnet 177 : -0.4102024 
nnet mean 177 : -0.3920171 
nnet RMSE 177 : 0.1448807 


s: 178 
logit 178 : -0.4966694 
logit mean 178 : -0.4390462 
logit RMSE 178 : 0.06937932 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 178 : -0.8320628 
boosting mean 178 : -0.4817845 
boosting RMSE 178 : 0.1394344 

forest 178 : -0.4013555 
forest mean 178 : -0.3894517 
forest RMSE 178 : 0.05106654 

nnet 178 : -0.3647418 
nnet mean 178 : -0.3918639 
nnet RMSE 178 : 0.1444973 


s: 179 
logit 179 : -0.5374283 
logit mean 179 : -0.4395958 
logit RMSE 179 : 0.06994362 

boosting 179 : -0.4838431 
boosting mean 179 : -0.481796 
boosting RMSE 179 : 0.1391855 

forest 179 : -0.4132516 
forest mean 179 : -0.3895846 
forest RMSE 179 : 0.05093333 

nnet 179 : -0.4792544 
nnet mean 179 : -0.3923521 
nnet RMSE 179 : 0.1442148 


s: 180 
logit 180 : -0.5027744 
logit mean 180 : -0.4399468 
logit RMSE 180 : 0.07016846 

boosting 180 : -0.5159438 
boosting mean 180 : -0.4819857 
boosting RMSE 180 : 0.1390671 

forest 180 : -0.4145383 
forest mean 180 : -0.3897233 
forest RMSE 180 : 0.05080321 

nnet 180 : -0.3651167 
nnet mean 180 : -0.3922008 
nnet RMSE 180 : 0.1438372 


s: 181 
logit 181 : -0.4542129 
logit mean 181 : -0.4400256 
logit RMSE 181 : 0.07009029 

boosting 181 : -0.635534 
boosting mean 181 : -0.482834 
boosting RMSE 181 : 0.1397831 

forest 181 : -0.4519327 
forest mean 181 : -0.3900670 
forest RMSE 181 : 0.05080952 

nnet 181 : -0.5947509 
nnet mean 181 : -0.3933199 
nnet RMSE 181 : 0.1441679 


s: 182 
logit 182 : -0.5366041 
logit mean 182 : -0.4405563 
logit RMSE 182 : 0.0706271 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 182 : -0.6683308 
boosting mean 182 : -0.4838532 
boosting RMSE 182 : 0.1408104 

forest 182 : -0.4190632 
forest mean 182 : -0.3902263 
forest RMSE 182 : 0.05068944 

nnet 182 : -0.298581 
nnet mean 182 : -0.3927993 
nnet RMSE 182 : 0.1439677 


s: 183 
logit 183 : -0.4409809 
logit mean 183 : -0.4405586 
logit RMSE 183 : 0.07049898 

boosting 183 : -0.3665042 
boosting mean 183 : -0.483212 
boosting RMSE 183 : 0.1404470 

forest 183 : -0.3793820 
forest mean 183 : -0.390167 
forest RMSE 183 : 0.05057372 

nnet 183 : -0.4317286 
nnet mean 183 : -0.3930121 
nnet RMSE 183 : 0.1435929 


s: 184 
logit 184 : -0.3670507 
logit mean 184 : -0.4401591 
logit RMSE 184 : 0.0703491 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 184 : -0.5365879 
boosting mean 184 : -0.4835021 
boosting RMSE 184 : 0.1404263 

forest 184 : -0.3472265 
forest mean 184 : -0.3899336 
forest RMSE 184 : 0.05058594 

nnet 184 : -0.413399 
nnet mean 184 : -0.3931229 
nnet RMSE 184 : 0.1432056 


s: 185 
logit 185 : -0.5755618 
logit mean 185 : -0.440891 
logit RMSE 185 : 0.07133617 

boosting 185 : -0.7401518 
boosting mean 185 : -0.4848894 
boosting RMSE 185 : 0.1422616 

forest 185 : -0.4043071 
forest mean 185 : -0.3900113 
forest RMSE 185 : 0.05045003 

nnet 185 : -0.2845532 
nnet mean 185 : -0.392536 
nnet RMSE 185 : 0.1430700 


s: 186 
logit 186 : -0.3480719 
logit mean 186 : -0.440392 
logit RMSE 186 : 0.07124596 

boosting 186 : -0.5463888 
boosting mean 186 : -0.48522 
boosting RMSE 186 : 0.1422842 

forest 186 : -0.3485865 
forest mean 186 : -0.3897886 
forest RMSE 186 : 0.05045525 

nnet 186 : -0.2978265 
nnet mean 186 : -0.3920268 
nnet RMSE 186 : 0.1428815 


s: 187 
logit 187 : -0.4761817 
logit mean 187 : -0.4405834 
logit RMSE 187 : 0.07127327 

boosting 187 : -0.4329924 
boosting mean 187 : -0.4849407 
boosting RMSE 187 : 0.1419237 

forest 187 : -0.4227735 
forest mean 187 : -0.389965 
forest RMSE 187 : 0.05034772 

nnet 187 : -0.3160932 
nnet mean 187 : -0.3916207 
nnet RMSE 187 : 0.1426310 


s: 188 
logit 188 : -0.517125 
logit mean 188 : -0.4409905 
logit RMSE 188 : 0.07159488 

boosting 188 : -0.4703554 
boosting mean 188 : -0.4848631 
boosting RMSE 188 : 0.1416387 

forest 188 : -0.356183 
forest mean 188 : -0.3897853 
forest RMSE 188 : 0.05031522 

nnet 188 : -0.422655 
nnet mean 188 : -0.3917858 
nnet RMSE 188 : 0.1422607 


s: 189 
logit 189 : -0.5311511 
logit mean 189 : -0.4414675 
logit RMSE 189 : 0.07203968 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 189 : -0.6126453 
boosting mean 189 : -0.4855392 
boosting RMSE 189 : 0.1421078 

forest 189 : -0.3265733 
forest mean 189 : -0.3894509 
forest RMSE 189 : 0.05046537 

nnet 189 : -0.5075751 
nnet mean 189 : -0.3923985 
nnet RMSE 189 : 0.1420995 


s: 190 
logit 190 : -0.3987414 
logit mean 190 : -0.4412427 
logit RMSE 190 : 0.0718499 

boosting 190 : -0.6068243 
boosting mean 190 : -0.4861776 
boosting RMSE 190 : 0.1425254 

forest 190 : -0.4052135 
forest mean 190 : -0.3895338 
forest RMSE 190 : 0.05033381 

nnet 190 : -0.6799022 
nnet mean 190 : -0.3939116 
nnet RMSE 190 : 0.1431724 


s: 191 
logit 191 : -0.4544555 
logit mean 191 : -0.4413118 
logit RMSE 191 : 0.07176982 

boosting 191 : -0.5379131 
boosting mean 191 : -0.4864484 
boosting RMSE 191 : 0.1425016 

forest 191 : -0.4057039 
forest mean 191 : -0.3896185 
forest RMSE 191 : 0.05020357 

nnet 191 : -0.5387591 
nnet mean 191 : -0.39467 
nnet RMSE 191 : 0.1431496 


s: 192 
logit 192 : -0.4587535 
logit mean 192 : -0.4414027 
logit RMSE 192 : 0.07170814 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 192 : -0.5279642 
boosting mean 192 : -0.4866647 
boosting RMSE 192 : 0.1424297 

forest 192 : -0.3196407 
forest mean 192 : -0.389254 
forest RMSE 192 : 0.05040739 

nnet 192 : -0.5488167 
nnet mean 192 : -0.3954728 
nnet RMSE 192 : 0.1431797 


s: 193 
logit 193 : -0.480197 
logit mean 193 : -0.4416037 
logit RMSE 193 : 0.07175472 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 193 : -0.5175354 
boosting mean 193 : -0.4868246 
boosting RMSE 193 : 0.1423120 

forest 193 : -0.3389872 
forest mean 193 : -0.3889936 
forest RMSE 193 : 0.05046808 

nnet 193 : -0.5478045 
nnet mean 193 : -0.3962621 
nnet RMSE 193 : 0.1432041 


s: 194 
logit 194 : -0.3762223 
logit mean 194 : -0.4412667 
logit RMSE 194 : 0.0715899 

boosting 194 : -0.5846931 
boosting mean 194 : -0.4873291 
boosting RMSE 194 : 0.1425628 

forest 194 : -0.3449634 
forest mean 194 : -0.3887666 
forest RMSE 194 : 0.05049269 

nnet 194 : -0.4045945 
nnet mean 194 : -0.3963051 
nnet RMSE 194 : 0.1428349 


s: 195 
logit 195 : -0.3958593 
logit mean 195 : -0.4410338 
logit RMSE 195 : 0.07140671 

boosting 195 : -0.4890898 
boosting mean 195 : -0.4873381 
boosting RMSE 195 : 0.1423398 

forest 195 : -0.37163 
forest mean 195 : -0.3886787 
forest RMSE 195 : 0.05040401 

nnet 195 : -0.3636374 
nnet mean 195 : -0.3961375 
nnet RMSE 195 : 0.1424920 


s: 196 
logit 196 : -0.3977712 
logit mean 196 : -0.4408131 
logit RMSE 196 : 0.0712245 

boosting 196 : -0.5179044 
boosting mean 196 : -0.4874941 
boosting RMSE 196 : 0.1422258 

forest 196 : -0.3906539 
forest mean 196 : -0.3886888 
forest RMSE 196 : 0.0502797 

nnet 196 : -0.4393805 
nnet mean 196 : -0.3963582 
nnet RMSE 196 : 0.1421558 


s: 197 
logit 197 : -0.3619468 
logit mean 197 : -0.4404128 
logit RMSE 197 : 0.07109521 

boosting 197 : -0.5042321 
boosting mean 197 : -0.4875791 
boosting RMSE 197 : 0.1420586 

forest 197 : -0.4886767 
forest mean 197 : -0.3891964 
forest RMSE 197 : 0.05054831 

nnet 197 : -0.6739112 
nnet mean 197 : -0.3977671 
nnet RMSE 197 : 0.1431312 


s: 198 
logit 198 : -0.3620054 
logit mean 198 : -0.4400168 
logit RMSE 198 : 0.07096684 

boosting 198 : -0.3615010 
boosting mean 198 : -0.4869423 
boosting RMSE 198 : 0.1417258 

forest 198 : -0.3238463 
forest mean 198 : -0.3888663 
forest RMSE 198 : 0.05071013 

nnet 198 : -0.4725918 
nnet mean 198 : -0.398145 
nnet RMSE 198 : 0.1428625 


s: 199 
logit 199 : -0.3625567 
logit mean 199 : -0.4396275 
logit RMSE 199 : 0.07083805 

boosting 199 : -0.3976735 
boosting mean 199 : -0.4864937 
boosting RMSE 199 : 0.1413693 

forest 199 : -0.4176894 
forest mean 199 : -0.3890112 
forest RMSE 199 : 0.0505981 

nnet 199 : -0.3335636 
nnet mean 199 : -0.3978204 
nnet RMSE 199 : 0.1425809 


s: 200 
logit 200 : -0.3826528 
logit mean 200 : -0.4393426 
logit RMSE 200 : 0.07067138 

boosting 200 : -0.4899334 
boosting mean 200 : -0.4865109 
boosting RMSE 200 : 0.1411588 

forest 200 : -0.3795010 
forest mean 200 : -0.3889636 
forest RMSE 200 : 0.05049225 

nnet 200 : -0.5571472 
nnet mean 200 : -0.3986171 
nnet RMSE 200 : 0.1426575 


s: 201 
logit 201 : -0.4918655 
logit mean 201 : -0.4396039 
logit RMSE 201 : 0.07079253 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 201 : -0.2163346 
boosting mean 201 : -0.4851667 
boosting RMSE 201 : 0.1414019 

forest 201 : -0.3460479 
forest mean 201 : -0.3887501 
forest RMSE 201 : 0.05051005 

nnet 201 : -0.4115418 
nnet mean 201 : -0.3986814 
nnet RMSE 201 : 0.1423045 


s: 202 
logit 202 : -0.4778705 
logit mean 202 : -0.4397934 
logit RMSE 202 : 0.07082931 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 202 : -0.4653907 
boosting mean 202 : -0.4850688 
boosting RMSE 202 : 0.1411265 

forest 202 : -0.3836616 
forest mean 202 : -0.3887249 
forest RMSE 202 : 0.05039799 

nnet 202 : -0.4444530 
nnet mean 202 : -0.398908 
nnet RMSE 202 : 0.1419862 


s: 203 
logit 203 : -0.4997128 
logit mean 203 : -0.4400886 
logit RMSE 203 : 0.0710004 

boosting 203 : -0.2772552 
boosting mean 203 : -0.4840451 
boosting RMSE 203 : 0.1410418 

forest 203 : -0.3691483 
forest mean 203 : -0.3886285 
forest RMSE 203 : 0.05032031 

nnet 203 : 0.02933166 
nnet mean 203 : -0.3967984 
nnet RMSE 203 : 0.1448060 


s: 204 
logit 204 : -0.3761118 
logit mean 204 : -0.4397749 
logit RMSE 204 : 0.07084591 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 204 : -0.3878111 
boosting mean 204 : -0.4835734 
boosting RMSE 204 : 0.1406983 

forest 204 : -0.2841602 
forest mean 204 : -0.3881164 
forest RMSE 204 : 0.05084781 

nnet 204 : -0.2230818 
nnet mean 204 : -0.3959469 
nnet RMSE 204 : 0.1449808 


s: 205 
logit 205 : -0.5073909 
logit mean 205 : -0.4401048 
logit RMSE 205 : 0.0710698 

boosting 205 : -0.4450426 
boosting mean 205 : -0.4833854 
boosting RMSE 205 : 0.1403899 

forest 205 : -0.4072577 
forest mean 205 : -0.3882097 
forest RMSE 205 : 0.05072617 

nnet 205 : -0.5920679 
nnet mean 205 : -0.3969036 
nnet RMSE 205 : 0.1452476 


s: 206 
logit 206 : -0.5017702 
logit mean 206 : -0.4404041 
logit RMSE 206 : 0.07125079 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 206 : -0.6013902 
boosting mean 206 : -0.4839583 
boosting RMSE 206 : 0.1407499 

forest 206 : -0.4589023 
forest mean 206 : -0.3885529 
forest RMSE 206 : 0.05076904 

nnet 206 : -0.2499962 
nnet mean 206 : -0.3961904 
nnet RMSE 206 : 0.1452710 


s: 207 
logit 207 : -0.3859090 
logit mean 207 : -0.4401409 
logit RMSE 207 : 0.07108523 

boosting 207 : -0.4656761 
boosting mean 207 : -0.48387 
boosting RMSE 207 : 0.1404837 

forest 207 : -0.3955038 
forest mean 207 : -0.3885865 
forest RMSE 207 : 0.05064723 

nnet 207 : -0.1745782 
nnet mean 207 : -0.3951198 
nnet RMSE 207 : 0.1457642 


s: 208 
logit 208 : -0.4755058 
logit mean 208 : -0.4403109 
logit RMSE 208 : 0.07110714 

boosting 208 : -0.3882598 
boosting mean 208 : -0.4834103 
boosting RMSE 208 : 0.1401480 

forest 208 : -0.3743821 
forest mean 208 : -0.3885182 
forest RMSE 208 : 0.05055655 

nnet 208 : -0.4023308 
nnet mean 208 : -0.3951545 
nnet RMSE 208 : 0.1454135 


s: 209 
logit 209 : -0.4360198 
logit mean 209 : -0.4402904 
logit RMSE 209 : 0.07098056 

boosting 209 : -0.3080402 
boosting mean 209 : -0.4825712 
boosting RMSE 209 : 0.1399569 

forest 209 : -0.4279507 
forest mean 209 : -0.3887069 
forest RMSE 209 : 0.0504725 

nnet 209 : -0.1261278 
nnet mean 209 : -0.3938673 
nnet RMSE 209 : 0.1462969 


s: 210 
logit 210 : -0.6045436 
logit mean 210 : -0.4410725 
logit RMSE 210 : 0.07220441 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 210 : -0.3057955 
boosting mean 210 : -0.4817294 
boosting RMSE 210 : 0.1397745 

forest 210 : -0.4078273 
forest mean 210 : -0.3887979 
forest RMSE 210 : 0.05035508 

nnet 210 : -0.3091082 
nnet mean 210 : -0.3934637 
nnet RMSE 210 : 0.1460829 


s: 211 
logit 211 : -0.4444301 
logit mean 211 : -0.4410884 
logit RMSE 211 : 0.07209802 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 211 : -0.3348826 
boosting mean 211 : -0.4810335 
boosting RMSE 211 : 0.1395150 

forest 211 : -0.4824606 
forest mean 211 : -0.3892418 
forest RMSE 211 : 0.05055535 

nnet 211 : -0.2826657 
nnet mean 211 : -0.3929386 
nnet RMSE 211 : 0.1459600 


s: 212 
logit 212 : -0.4288857 
logit mean 212 : -0.4410309 
logit RMSE 212 : 0.07195513 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 212 : -0.6792201 
boosting mean 212 : -0.4819683 
boosting RMSE 212 : 0.1405004 

forest 212 : -0.304091 
forest mean 212 : -0.3888402 
forest RMSE 212 : 0.05086429 

nnet 212 : -0.3653416 
nnet mean 212 : -0.3928084 
nnet RMSE 212 : 0.1456348 


s: 213 
logit 213 : -0.4682704 
logit mean 213 : -0.4411587 
logit RMSE 213 : 0.07193827 

boosting 213 : -0.5380052 
boosting mean 213 : -0.4822314 
boosting RMSE 213 : 0.1404888 

forest 213 : -0.3867992 
forest mean 213 : -0.3888306 
forest RMSE 213 : 0.05075282 

nnet 213 : -0.5392842 
nnet mean 213 : -0.3934961 
nnet RMSE 213 : 0.1456056 


s: 214 
logit 214 : -0.3984094 
logit mean 214 : -0.440959 
logit RMSE 214 : 0.07177008 

boosting 214 : -0.4287246 
boosting mean 214 : -0.4819814 
boosting RMSE 214 : 0.1401739 

forest 214 : -0.3770491 
forest mean 214 : -0.3887755 
forest RMSE 214 : 0.0506584 

nnet 214 : -0.3406814 
nnet mean 214 : -0.3932493 
nnet RMSE 214 : 0.1453216 


s: 215 
logit 215 : -0.4627431 
logit mean 215 : -0.4410603 
logit RMSE 215 : 0.07173072 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 215 : -0.3022866 
boosting mean 215 : -0.4811456 
boosting RMSE 215 : 0.1400063 

forest 215 : -0.3933948 
forest mean 215 : -0.388797 
forest RMSE 215 : 0.05054245 

nnet 215 : -0.4449637 
nnet mean 215 : -0.3934898 
nnet RMSE 215 : 0.1450157 


s: 216 
logit 216 : -0.4737097 
logit mean 216 : -0.4412115 
logit RMSE 216 : 0.07174001 

boosting 216 : -0.674684 
boosting mean 216 : -0.4820416 
boosting RMSE 216 : 0.1409266 

forest 216 : -0.4109431 
forest mean 216 : -0.3888995 
forest RMSE 216 : 0.05043082 

nnet 216 : -0.5752969 
nnet mean 216 : -0.3943315 
nnet RMSE 216 : 0.1451704 


s: 217 
logit 217 : -0.5429628 
logit mean 217 : -0.4416804 
logit RMSE 217 : 0.07222948 

boosting 217 : -0.4586936 
boosting mean 217 : -0.481934 
boosting RMSE 217 : 0.1406580 

forest 217 : -0.4112707 
forest mean 217 : -0.3890026 
forest RMSE 217 : 0.0503203 

nnet 217 : -0.470023 
nnet mean 217 : -0.3946803 
nnet RMSE 217 : 0.1449135 


s: 218 
logit 218 : -0.3765335 
logit mean 218 : -0.4413815 
logit RMSE 218 : 0.07208115 

boosting 218 : -0.549468 
boosting mean 218 : -0.4822438 
boosting RMSE 218 : 0.1406997 

forest 218 : -0.3319575 
forest mean 218 : -0.3887410 
forest RMSE 218 : 0.05041582 

nnet 218 : -0.3505703 
nnet mean 218 : -0.394478 
nnet RMSE 218 : 0.1446195 


s: 219 
logit 219 : -0.4002005 
logit mean 219 : -0.4411935 
logit RMSE 219 : 0.0719164 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 219 : -0.2766487 
boosting mean 219 : -0.481305 
boosting RMSE 219 : 0.1406253 

forest 219 : -0.2837852 
forest mean 219 : -0.3882617 
forest RMSE 219 : 0.05090992 

nnet 219 : -0.3896533 
nnet mean 219 : -0.3944559 
nnet RMSE 219 : 0.1442907 


s: 220 
logit 220 : -0.4545575 
logit mean 220 : -0.4412542 
logit RMSE 220 : 0.07184698 

boosting 220 : -0.3701661 
boosting mean 220 : -0.4807998 
boosting RMSE 220 : 0.1403197 

forest 220 : -0.3603658 
forest mean 220 : -0.3881349 
forest RMSE 220 : 0.05086432 

nnet 220 : -0.3849057 
nnet mean 220 : -0.3944125 
nnet RMSE 220 : 0.1439660 


s: 221 
logit 221 : -0.3353123 
logit mean 221 : -0.4407749 
logit RMSE 221 : 0.07181619 

boosting 221 : -0.7072633 
boosting mean 221 : -0.4818245 
boosting RMSE 221 : 0.1415194 

forest 221 : -0.4227698 
forest mean 221 : -0.3882916 
forest RMSE 221 : 0.05077222 

nnet 221 : -0.4403568 
nnet mean 221 : -0.3946204 
nnet RMSE 221 : 0.1436655 


s: 222 
logit 222 : -0.5870097 
logit mean 222 : -0.4414336 
logit RMSE 222 : 0.07274522 

boosting 222 : -0.6155576 
boosting mean 222 : -0.4824269 
boosting RMSE 222 : 0.1419395 

forest 222 : -0.3512827 
forest mean 222 : -0.3881249 
forest RMSE 222 : 0.05076315 

nnet 222 : -0.3589070 
nnet mean 222 : -0.3944596 
nnet RMSE 222 : 0.1433681 


s: 223 
logit 223 : -0.3655087 
logit mean 223 : -0.4410931 
logit RMSE 223 : 0.07261867 

boosting 223 : -0.4783587 
boosting mean 223 : -0.4824087 
boosting RMSE 223 : 0.1417181 

forest 223 : -0.4051735 
forest mean 223 : -0.3882014 
forest RMSE 223 : 0.05065039 

nnet 223 : -0.3830414 
nnet mean 223 : -0.3944083 
nnet RMSE 223 : 0.1430508 


s: 224 
logit 224 : -0.3997946 
logit mean 224 : -0.4409087 
logit RMSE 224 : 0.0724564 

boosting 224 : -0.4731824 
boosting mean 224 : -0.4823675 
boosting RMSE 224 : 0.1414859 

forest 224 : -0.3467335 
forest mean 224 : -0.3880162 
forest RMSE 224 : 0.05066237 

nnet 224 : -0.213167 
nnet mean 224 : -0.3935992 
nnet RMSE 224 : 0.143276 


s: 225 
logit 225 : -0.4333158 
logit mean 225 : -0.440875 
logit RMSE 225 : 0.07232932 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 225 : -0.7024758 
boosting mean 225 : -0.4833458 
boosting RMSE 225 : 0.1426041 

forest 225 : -0.4035959 
forest mean 225 : -0.3880855 
forest RMSE 225 : 0.05055023 

nnet 225 : -0.5147856 
nnet mean 225 : -0.3941378 
nnet RMSE 225 : 0.1431619 


s: 226 
logit 226 : -0.4202384 
logit mean 226 : -0.4407837 
logit RMSE 226 : 0.07218167 

boosting 226 : -0.3747064 
boosting mean 226 : -0.482865 
boosting RMSE 226 : 0.1422982 

forest 226 : -0.3509478 
forest mean 226 : -0.3879212 
forest RMSE 226 : 0.0505437 

nnet 226 : -0.5034052 
nnet mean 226 : -0.3946213 
nnet RMSE 226 : 0.1430103 


s: 227 
logit 227 : -0.5056186 
logit mean 227 : -0.4410693 
logit RMSE 227 : 0.07236286 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 227 : -0.2684075 
boosting mean 227 : -0.4819203 
boosting RMSE 227 : 0.1422528 

forest 227 : -0.4544422 
forest mean 227 : -0.3882142 
forest RMSE 227 : 0.05056153 

nnet 227 : -0.4931797 
nnet mean 227 : -0.3950555 
nnet RMSE 227 : 0.1428290 


s: 228 
logit 228 : -0.4730534 
logit mean 228 : -0.4412096 
logit RMSE 228 : 0.0723659 

boosting 228 : -0.3595188 
boosting mean 228 : -0.4813835 
boosting RMSE 228 : 0.1419658 

forest 228 : -0.3263369 
forest mean 228 : -0.3879428 
forest RMSE 228 : 0.05068585 

nnet 228 : -0.4616345 
nnet mean 228 : -0.3953475 
nnet RMSE 228 : 0.1425738 


s: 229 
logit 229 : -0.3707791 
logit mean 229 : -0.440902 
logit RMSE 229 : 0.07223354 

boosting 229 : -0.4347578 
boosting mean 229 : -0.4811798 
boosting RMSE 229 : 0.1416741 

forest 229 : -0.4250929 
forest mean 229 : -0.3881050 
forest RMSE 229 : 0.05060223 

nnet 229 : -0.3903414 
nnet mean 229 : -0.3953257 
nnet RMSE 229 : 0.1422636 


s: 230 
logit 230 : -0.4507492 
logit mean 230 : -0.4409448 
logit RMSE 230 : 0.07215398 

boosting 230 : -0.5517202 
boosting mean 230 : -0.4814865 
boosting RMSE 230 : 0.1417193 

forest 230 : -0.339461 
forest mean 230 : -0.3878935 
forest RMSE 230 : 0.05064966 

nnet 230 : -0.4012192 
nnet mean 230 : -0.3953513 
nnet RMSE 230 : 0.1419540 


s: 231 
logit 231 : -0.4519545 
logit mean 231 : -0.4409925 
logit RMSE 231 : 0.07207874 

boosting 231 : -0.4256063 
boosting mean 231 : -0.4812446 
boosting RMSE 231 : 0.1414223 

forest 231 : -0.4075562 
forest mean 231 : -0.3879787 
forest RMSE 231 : 0.05054235 

nnet 231 : -0.2008859 
nnet mean 231 : -0.3945094 
nnet RMSE 231 : 0.142251 


s: 232 
logit 232 : -0.4476709 
logit mean 232 : -0.4410213 
logit RMSE 232 : 0.07199129 

boosting 232 : -0.3931443 
boosting mean 232 : -0.4808649 
boosting RMSE 232 : 0.1411179 

forest 232 : -0.4022494 
forest mean 232 : -0.3880402 
forest RMSE 232 : 0.05043352 

nnet 232 : -0.3783116 
nnet mean 232 : -0.3944396 
nnet RMSE 232 : 0.1419512 


s: 233 
logit 233 : -0.3212448 
logit mean 233 : -0.4405072 
logit RMSE 233 : 0.07202168 

boosting 233 : -0.399384 
boosting mean 233 : -0.4805152 
boosting RMSE 233 : 0.1408147 

forest 233 : -0.3812796 
forest mean 233 : -0.3880112 
forest RMSE 233 : 0.05034012 

nnet 233 : -0.3864672 
nnet mean 233 : -0.3944054 
nnet RMSE 233 : 0.1416491 


s: 234 
logit 234 : -0.4413237 
logit mean 234 : -0.4405107 
logit RMSE 234 : 0.07191837 

boosting 234 : -0.5574218 
boosting mean 234 : -0.4808439 
boosting RMSE 234 : 0.1408899 

forest 234 : -0.3511662 
forest mean 234 : -0.3878537 
forest RMSE 234 : 0.05033378 

nnet 234 : -0.7046071 
nnet mean 234 : -0.3957311 
nnet RMSE 234 : 0.1427418 


s: 235 
logit 235 : -0.4510276 
logit mean 235 : -0.4405555 
logit RMSE 235 : 0.07184235 

boosting 235 : -0.6765712 
boosting mean 235 : -0.4816767 
boosting RMSE 235 : 0.1417427 

forest 235 : -0.3134475 
forest mean 235 : -0.3875371 
forest RMSE 235 : 0.05054292 

nnet 235 : -0.4888577 
nnet mean 235 : -0.3961273 
nnet RMSE 235 : 0.1425557 


s: 236 
logit 236 : -0.3634972 
logit mean 236 : -0.4402289 
logit RMSE 236 : 0.07172934 

boosting 236 : -0.5087455 
boosting mean 236 : -0.4817914 
boosting RMSE 236 : 0.1416191 

forest 236 : -0.4868126 
forest mean 236 : -0.3879577 
forest RMSE 236 : 0.05075132 

nnet 236 : -0.3425702 
nnet mean 236 : -0.3959004 
nnet RMSE 236 : 0.1423025 


s: 237 
logit 237 : -0.4079446 
logit mean 237 : -0.4400927 
logit RMSE 237 : 0.07157972 

boosting 237 : -0.4258652 
boosting mean 237 : -0.4815555 
boosting RMSE 237 : 0.1413300 

forest 237 : -0.4480133 
forest mean 237 : -0.3882111 
forest RMSE 237 : 0.05074007 

nnet 237 : -0.4658255 
nnet mean 237 : -0.3961954 
nnet RMSE 237 : 0.1420663 


s: 238 
logit 238 : -0.3995422 
logit mean 238 : -0.4399223 
logit RMSE 238 : 0.07142919 

boosting 238 : -0.4040512 
boosting mean 238 : -0.4812298 
boosting RMSE 238 : 0.1410330 

forest 238 : -0.3890398 
forest mean 238 : -0.3882146 
forest RMSE 238 : 0.05063835 

nnet 238 : -0.4516002 
nnet mean 238 : -0.3964282 
nnet RMSE 238 : 0.1418070 


s: 239 
logit 239 : -0.4060424 
logit mean 239 : -0.4397806 
logit RMSE 239 : 0.07128067 

boosting 239 : -0.6887161 
boosting mean 239 : -0.4820980 
boosting RMSE 239 : 0.1419713 

forest 239 : -0.4233657 
forest mean 239 : -0.3883617 
forest RMSE 239 : 0.0505549 

nnet 239 : -0.5028427 
nnet mean 239 : -0.3968735 
nnet RMSE 239 : 0.1416663 


s: 240 
logit 240 : -0.419103 
logit mean 240 : -0.4396944 
logit RMSE 240 : 0.0711427 

boosting 240 : -0.5662405 
boosting mean 240 : -0.4824485 
boosting RMSE 240 : 0.1420810 

forest 240 : -0.3579064 
forest mean 240 : -0.3882348 
forest RMSE 240 : 0.05052258 

nnet 240 : -0.4263437 
nnet mean 240 : -0.3969963 
nnet RMSE 240 : 0.1413811 


s: 241 
logit 241 : -0.4365241 
logit mean 241 : -0.4396813 
logit RMSE 241 : 0.07103392 

boosting 241 : -0.2616063 
boosting mean 241 : -0.4815322 
boosting RMSE 241 : 0.1420659 

forest 241 : -0.4004433 
forest mean 241 : -0.3882855 
forest RMSE 241 : 0.05041766 

nnet 241 : -0.4813861 
nnet mean 241 : -0.3973464 
nnet RMSE 241 : 0.1411848 


s: 242 
logit 242 : -0.3923296 
logit mean 242 : -0.4394856 
logit RMSE 242 : 0.07088872 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 242 : -0.4344768 
boosting mean 242 : -0.4813377 
boosting RMSE 242 : 0.1417894 

forest 242 : -0.4085394 
forest mean 242 : -0.3883692 
forest RMSE 242 : 0.05031638 

nnet 242 : -0.6445363 
nnet mean 242 : -0.3983679 
nnet RMSE 242 : 0.1417670 


s: 243 
logit 243 : -0.359162 
logit mean 243 : -0.4391550 
logit RMSE 243 : 0.0707912 

boosting 243 : -0.5637049 
boosting mean 243 : -0.4816767 
boosting RMSE 243 : 0.1418865 

forest 243 : -0.3896845 
forest mean 243 : -0.3883746 
forest RMSE 243 : 0.0502171 

nnet 243 : -0.5614381 
nnet mean 243 : -0.3990390 
nnet RMSE 243 : 0.1418535 


s: 244 
logit 244 : -0.3720428 
logit mean 244 : -0.43888 
logit RMSE 244 : 0.07066865 

boosting 244 : -0.3173133 
boosting mean 244 : -0.4810031 
boosting RMSE 244 : 0.1416944 

forest 244 : -0.4741854 
forest mean 244 : -0.3887263 
forest RMSE 244 : 0.05033863 

nnet 244 : -0.5349724 
nnet mean 244 : -0.3995961 
nnet RMSE 244 : 0.141826 


s: 245 
logit 245 : -0.4586772 
logit mean 245 : -0.4389608 
logit RMSE 245 : 0.07062385 

boosting 245 : -0.5511939 
boosting mean 245 : -0.4812896 
boosting RMSE 245 : 0.1417345 

forest 245 : -0.3842092 
forest mean 245 : -0.3887078 
forest RMSE 245 : 0.05024592 

nnet 245 : -0.8368155 
nnet mean 245 : -0.4013806 
nnet RMSE 245 : 0.1442613 


s: 246 
logit 246 : -0.4087329 
logit mean 246 : -0.4388379 
logit RMSE 246 : 0.07048236 

boosting 246 : -0.6133775 
boosting mean 246 : -0.4818265 
boosting RMSE 246 : 0.1420988 

forest 246 : -0.3525593 
forest mean 246 : -0.3885609 
forest RMSE 246 : 0.05023483 

nnet 246 : -0.3825016 
nnet mean 246 : -0.4013039 
nnet RMSE 246 : 0.1439721 


s: 247 
logit 247 : -0.4932303 
logit mean 247 : -0.4390581 
logit RMSE 247 : 0.07058923 

boosting 247 : -0.3626355 
boosting mean 247 : -0.481344 
boosting RMSE 247 : 0.1418308 

forest 247 : -0.4365589 
forest mean 247 : -0.3887552 
forest RMSE 247 : 0.05018698 

nnet 247 : -0.3506245 
nnet mean 247 : -0.4010987 
nnet RMSE 247 : 0.1437147 


s: 248 
logit 248 : -0.4844418 
logit mean 248 : -0.4392411 
logit RMSE 248 : 0.07065055 

boosting 248 : -0.5806645 
boosting mean 248 : -0.4817444 
boosting RMSE 248 : 0.1420087 

forest 248 : -0.3692182 
forest mean 248 : -0.3886764 
forest RMSE 248 : 0.05012382 

nnet 248 : -0.4061098 
nnet mean 248 : -0.4011189 
nnet RMSE 248 : 0.1434252 


s: 249 
logit 249 : -0.4472523 
logit mean 249 : -0.4392733 
logit RMSE 249 : 0.0705721 

boosting 249 : -0.3659747 
boosting mean 249 : -0.4812795 
boosting RMSE 249 : 0.1417397 

forest 249 : -0.3745757 
forest mean 249 : -0.3886198 
forest RMSE 249 : 0.05004901 

nnet 249 : -0.3568477 
nnet mean 249 : -0.4009411 
nnet RMSE 249 : 0.1431630 


s: 250 
logit 250 : -0.4996131 
logit mean 250 : -0.4395147 
logit RMSE 250 : 0.07071202 

boosting 250 : -0.3914219 
boosting mean 250 : -0.4809201 
boosting RMSE 250 : 0.1414570 

forest 250 : -0.3450782 
forest mean 250 : -0.3884456 
forest RMSE 250 : 0.05006945 

nnet 250 : -0.3073837 
nnet mean 250 : -0.4005669 
nnet RMSE 250 : 0.1429964 


s: 251 
logit 251 : -0.4094858 
logit mean 251 : -0.439395 
logit RMSE 251 : 0.07057356 

boosting 251 : -0.1715181 
boosting mean 251 : -0.4796874 
boosting RMSE 251 : 0.1419096 

forest 251 : -0.4014537 
forest mean 251 : -0.3884974 
forest RMSE 251 : 0.04996969 

nnet 251 : -0.7063042 
nnet mean 251 : -0.401785 
nnet RMSE 251 : 0.1440150 


s: 252 
logit 252 : -0.4635293 
logit mean 252 : -0.4394908 
logit RMSE 252 : 0.070547 

boosting 252 : -0.5988821 
boosting mean 252 : -0.4801604 
boosting RMSE 252 : 0.1421808 

forest 252 : -0.403232 
forest mean 252 : -0.3885559 
forest RMSE 252 : 0.04987086 

nnet 252 : -0.3140455 
nnet mean 252 : -0.4014368 
nnet RMSE 252 : 0.1438309 


s: 253 
logit 253 : -0.4638284 
logit mean 253 : -0.439587 
logit RMSE 253 : 0.0705217 

boosting 253 : -0.4566714 
boosting mean 253 : -0.4800676 
boosting RMSE 253 : 0.1419443 

forest 253 : -0.4410925 
forest mean 253 : -0.3887636 
forest RMSE 253 : 0.04983921 

nnet 253 : -0.5233926 
nnet mean 253 : -0.4019188 
nnet RMSE 253 : 0.1437558 


s: 254 
logit 254 : -0.5457353 
logit mean 254 : -0.4400049 
logit RMSE 254 : 0.07097427 

boosting 254 : -0.3473858 
boosting mean 254 : -0.4795452 
boosting RMSE 254 : 0.1417030 

forest 254 : -0.4373659 
forest mean 254 : -0.3889549 
forest RMSE 254 : 0.04979623 

nnet 254 : -0.3744525 
nnet mean 254 : -0.4018107 
nnet RMSE 254 : 0.1434815 


s: 255 
logit 255 : -0.5042203 
logit mean 255 : -0.4402567 
logit RMSE 255 : 0.071135 

boosting 255 : -0.6902773 
boosting mean 255 : -0.4803716 
boosting RMSE 255 : 0.1425884 

forest 255 : -0.4345594 
forest mean 255 : -0.3891338 
forest RMSE 255 : 0.04974559 

nnet 255 : -0.5753294 
nnet mean 255 : -0.4024912 
nnet RMSE 255 : 0.1436202 


s: 256 
logit 256 : -0.4326694 
logit mean 256 : -0.4402271 
logit RMSE 256 : 0.07102529 

boosting 256 : -0.5605362 
boosting mean 256 : -0.4806847 
boosting RMSE 256 : 0.1426629 

forest 256 : -0.3361949 
forest mean 256 : -0.388927 
forest RMSE 256 : 0.04980823 

nnet 256 : -0.505087 
nnet mean 256 : -0.4028919 
nnet RMSE 256 : 0.1434898 


s: 257 
logit 257 : -0.4990078 
logit mean 257 : -0.4404558 
logit RMSE 257 : 0.0711555 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 257 : -0.5975584 
boosting mean 257 : -0.4811395 
boosting RMSE 257 : 0.1429173 

forest 257 : -0.3843901 
forest mean 257 : -0.3889093 
forest RMSE 257 : 0.04972077 

nnet 257 : -0.4723002 
nnet mean 257 : -0.403162 
nnet RMSE 257 : 0.1432814 


s: 258 
logit 258 : -0.4836725 
logit mean 258 : -0.4406233 
logit RMSE 258 : 0.07120826 

boosting 258 : -0.5794465 
boosting mean 258 : -0.4815205 
boosting RMSE 258 : 0.1430769 

forest 258 : -0.3959442 
forest mean 258 : -0.3889366 
forest RMSE 258 : 0.04962496 

nnet 258 : -0.5134883 
nnet mean 258 : -0.4035896 
nnet RMSE 258 : 0.1431779 


s: 259 
logit 259 : -0.4334711 
logit mean 259 : -0.4405957 
logit RMSE 259 : 0.07110108 

boosting 259 : -0.4078525 
boosting mean 259 : -0.4812361 
boosting RMSE 259 : 0.1428013 

forest 259 : -0.2761450 
forest mean 259 : -0.3885011 
forest RMSE 259 : 0.05012341 

nnet 259 : -0.5313489 
nnet mean 259 : -0.4040829 
nnet RMSE 259 : 0.1431341 


s: 260 
logit 260 : -0.3665555 
logit mean 260 : -0.4403109 
logit RMSE 260 : 0.07099453 

boosting 260 : -0.3978603 
boosting mean 260 : -0.4809154 
boosting RMSE 260 : 0.1425265 

forest 260 : -0.3412733 
forest mean 260 : -0.3883194 
forest RMSE 260 : 0.05015933 

nnet 260 : -0.2230652 
nnet mean 260 : -0.4033867 
nnet RMSE 260 : 0.1432794 


s: 261 
logit 261 : -0.4123757 
logit mean 261 : -0.4402039 
logit RMSE 261 : 0.07086253 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 261 : -0.3782333 
boosting mean 261 : -0.480522 
boosting RMSE 261 : 0.1422595 

forest 261 : -0.3898905 
forest mean 261 : -0.3883255 
forest RMSE 261 : 0.05006706 

nnet 261 : -0.5603723 
nnet mean 261 : -0.4039882 
nnet RMSE 261 : 0.1433487 


s: 262 
logit 262 : -0.5082166 
logit mean 262 : -0.4404635 
logit RMSE 262 : 0.07104245 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 262 : -0.46737 
boosting mean 262 : -0.4804718 
boosting RMSE 262 : 0.1420488 

forest 262 : -0.3791392 
forest mean 262 : -0.3882904 
forest RMSE 262 : 0.04998803 

nnet 262 : -0.5297996 
nnet mean 262 : -0.4044683 
nnet RMSE 262 : 0.1432995 


s: 263 
logit 263 : -0.412319 
logit mean 263 : -0.4403565 
logit RMSE 263 : 0.07091133 

boosting 263 : -0.3848719 
boosting mean 263 : -0.4801083 
boosting RMSE 263 : 0.1417815 

forest 263 : -0.4951966 
forest mean 263 : -0.3886969 
forest RMSE 263 : 0.05023704 

nnet 263 : -0.4945796 
nnet mean 263 : -0.404811 
nnet RMSE 263 : 0.1431456 


s: 264 
logit 264 : -0.3532571 
logit mean 264 : -0.4400266 
logit RMSE 264 : 0.07083534 

boosting 264 : -0.5069701 
boosting mean 264 : -0.4802101 
boosting RMSE 264 : 0.1416658 

forest 264 : -0.3784088 
forest mean 264 : -0.3886579 
forest RMSE 264 : 0.05015941 

nnet 264 : -0.372842 
nnet mean 264 : -0.4046899 
nnet RMSE 264 : 0.1428840 


s: 265 
logit 265 : -0.437209 
logit mean 265 : -0.4400159 
logit RMSE 265 : 0.0707385 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 265 : -0.2744501 
boosting mean 265 : -0.4794336 
boosting RMSE 265 : 0.1416085 

forest 265 : -0.4476224 
forest mean 265 : -0.3888804 
forest RMSE 265 : 0.05015008 

nnet 265 : -0.2925413 
nnet mean 265 : -0.4042667 
nnet RMSE 265 : 0.1427669 


s: 266 
logit 266 : -0.4041830 
logit mean 266 : -0.4398812 
logit RMSE 266 : 0.07060588 

boosting 266 : -0.5028317 
boosting mean 266 : -0.4795216 
boosting RMSE 266 : 0.1414826 

forest 266 : -0.430251 
forest mean 266 : -0.3890360 
forest RMSE 266 : 0.05009007 

nnet 266 : -0.3305992 
nnet mean 266 : -0.4039897 
nnet RMSE 266 : 0.1425618 


s: 267 
logit 267 : -0.3922659 
logit mean 267 : -0.4397029 
logit RMSE 267 : 0.07047512 

boosting 267 : -0.4446295 
boosting mean 267 : -0.4793909 
boosting RMSE 267 : 0.1412438 

forest 267 : -0.3766679 
forest mean 267 : -0.3889896 
forest RMSE 267 : 0.05001657 

nnet 267 : -0.7776295 
nnet mean 267 : -0.4053891 
nnet RMSE 267 : 0.1441591 


s: 268 
logit 268 : -0.3263185 
logit mean 268 : -0.4392798 
logit RMSE 268 : 0.07048736 

boosting 268 : -0.3585545 
boosting mean 268 : -0.47894 
boosting RMSE 268 : 0.1410028 

forest 268 : -0.3754985 
forest mean 268 : -0.3889393 
forest RMSE 268 : 0.0499456 

nnet 268 : -0.6428167 
nnet mean 268 : -0.4062751 
nnet RMSE 268 : 0.1446523 


s: 269 
logit 269 : -0.4487156 
logit mean 269 : -0.4393149 
logit RMSE 269 : 0.07041889 

boosting 269 : -0.7731857 
boosting mean 269 : -0.4800338 
boosting RMSE 269 : 0.1425679 

forest 269 : -0.4022566 
forest mean 269 : -0.3889888 
forest RMSE 269 : 0.04985287 

nnet 269 : -0.3723334 
nnet mean 269 : -0.4061489 
nnet RMSE 269 : 0.1443931 


s: 270 
logit 270 : -0.3411736 
logit mean 270 : -0.4389514 
logit RMSE 270 : 0.07037948 

boosting 270 : -0.4692038 
boosting mean 270 : -0.4799937 
boosting RMSE 270 : 0.1423659 

forest 270 : -0.4129094 
forest mean 270 : -0.3890774 
forest RMSE 270 : 0.04976666 

nnet 270 : -0.2908986 
nnet mean 270 : -0.405722 
nnet RMSE 270 : 0.1442783 


s: 271 
logit 271 : -0.426625 
logit mean 271 : -0.4389059 
logit RMSE 271 : 0.07026812 

boosting 271 : -0.5024308 
boosting mean 271 : -0.4800765 
boosting RMSE 271 : 0.1422392 

forest 271 : -0.4799633 
forest mean 271 : -0.3894128 
forest RMSE 271 : 0.04991168 

nnet 271 : -0.4513057 
nnet mean 271 : -0.4058902 
nnet RMSE 271 : 0.1440456 


s: 272 
logit 272 : -0.4761638 
logit mean 272 : -0.4390429 
logit RMSE 272 : 0.0702907 

boosting 272 : -0.454085 
boosting mean 272 : -0.479981 
boosting RMSE 272 : 0.1420153 

forest 272 : -0.2719025 
forest mean 272 : -0.3889807 
forest RMSE 272 : 0.05042167 

nnet 272 : -0.800871 
nnet mean 272 : -0.4073424 
nnet RMSE 272 : 0.1458206 


s: 273 
logit 273 : -0.3917704 
logit mean 273 : -0.4388697 
logit RMSE 273 : 0.07016361 

boosting 273 : -0.6008543 
boosting mean 273 : -0.4804237 
boosting RMSE 273 : 0.1422753 

forest 273 : -0.4478803 
forest mean 273 : -0.3891965 
forest RMSE 273 : 0.05041259 

nnet 273 : -0.4700254 
nnet mean 273 : -0.407572 
nnet RMSE 273 : 0.1456149 


s: 274 
logit 274 : -0.4079354 
logit mean 274 : -0.4387568 
logit RMSE 274 : 0.0700371 

boosting 274 : -0.251915 
boosting mean 274 : -0.4795898 
boosting RMSE 274 : 0.1422969 

forest 274 : -0.3164738 
forest mean 274 : -0.3889311 
forest RMSE 274 : 0.05057288 

nnet 274 : -0.3706735 
nnet mean 274 : -0.4074373 
nnet RMSE 274 : 0.1453598 


s: 275 
logit 275 : -0.3735455 
logit mean 275 : -0.4385197 
logit RMSE 275 : 0.06992784 

boosting 275 : -0.5774908 
boosting mean 275 : -0.4799458 
boosting RMSE 275 : 0.1424406 

forest 275 : -0.4538807 
forest mean 275 : -0.3891673 
forest RMSE 275 : 0.0505853 

nnet 275 : -0.2356419 
nnet mean 275 : -0.4068126 
nnet RMSE 275 : 0.1454334 


s: 276 
logit 276 : -0.3854575 
logit mean 276 : -0.4383274 
logit RMSE 276 : 0.06980654 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 276 : -0.353857 
boosting mean 276 : -0.4794889 
boosting RMSE 276 : 0.1422095 

forest 276 : -0.340057 
forest mean 276 : -0.3889893 
forest RMSE 276 : 0.05062233 

nnet 276 : -0.3447148 
nnet mean 276 : -0.4065876 
nnet RMSE 276 : 0.1452078 


s: 277 
logit 277 : -0.4594369 
logit mean 277 : -0.4384036 
logit RMSE 277 : 0.06977187 

boosting 277 : -0.4815047 
boosting mean 277 : -0.4794962 
boosting RMSE 277 : 0.1420370 

forest 277 : -0.4482878 
forest mean 277 : -0.3892034 
forest RMSE 277 : 0.05061409 

nnet 277 : -0.5172716 
nnet mean 277 : -0.4069872 
nnet RMSE 277 : 0.1451166 


s: 278 
logit 278 : -0.3728488 
logit mean 278 : -0.4381678 
logit RMSE 278 : 0.0696653 

boosting 278 : -0.7682418 
boosting mean 278 : -0.4805349 
boosting RMSE 278 : 0.1434912 

forest 278 : -0.4468815 
forest mean 278 : -0.3894109 
forest RMSE 278 : 0.05060116 

nnet 278 : -0.7702659 
nnet mean 278 : -0.4082939 
nnet RMSE 278 : 0.1465477 


s: 279 
logit 279 : -0.3794863 
logit mean 279 : -0.4379575 
logit RMSE 279 : 0.06955119 

boosting 279 : -0.2456382 
boosting mean 279 : -0.4796929 
boosting RMSE 279 : 0.1435316 

forest 279 : -0.4238149 
forest mean 279 : -0.3895342 
forest RMSE 279 : 0.05053051 

nnet 279 : -0.239837 
nnet mean 279 : -0.4076902 
nnet RMSE 279 : 0.1465988 


s: 280 
logit 280 : -0.3935217 
logit mean 280 : -0.4377988 
logit RMSE 280 : 0.06942796 

boosting 280 : -0.3901793 
boosting mean 280 : -0.4793732 
boosting RMSE 280 : 0.1432763 

forest 280 : -0.4156877 
forest mean 280 : -0.3896276 
forest RMSE 280 : 0.05044891 

nnet 280 : -0.1134013 
nnet mean 280 : -0.4066391 
nnet RMSE 280 : 0.1473357 


s: 281 
logit 281 : -0.4566397 
logit mean 281 : -0.4378659 
logit RMSE 281 : 0.06938663 

boosting 281 : -0.528007 
boosting mean 281 : -0.4795463 
boosting RMSE 281 : 0.1432248 

forest 281 : -0.339288 
forest mean 281 : -0.3894484 
forest RMSE 281 : 0.05048913 

nnet 281 : -0.6145433 
nnet mean 281 : -0.407379 
nnet RMSE 281 : 0.1476291 


s: 282 
logit 282 : -0.4807443 
logit mean 282 : -0.4380179 
logit RMSE 282 : 0.06943019 

boosting 282 : -0.3262238 
boosting mean 282 : -0.4790026 
boosting RMSE 282 : 0.1430381 

forest 282 : -0.2870773 
forest mean 282 : -0.3890854 
forest RMSE 282 : 0.05084615 

nnet 282 : -0.5774875 
nnet mean 282 : -0.4079822 
nnet RMSE 282 : 0.1477456 


s: 283 
logit 283 : -0.4944778 
logit mean 283 : -0.4382174 
logit RMSE 283 : 0.06953458 

boosting 283 : -0.3377671 
boosting mean 283 : -0.4785035 
boosting RMSE 283 : 0.1428331 

forest 283 : -0.3973168 
forest mean 283 : -0.3891145 
forest RMSE 283 : 0.05075649 

nnet 283 : -0.4442156 
nnet mean 283 : -0.4081103 
nnet RMSE 283 : 0.1475078 


s: 284 
logit 284 : -0.4946122 
logit mean 284 : -0.438416 
logit RMSE 284 : 0.06963873 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 284 : -0.5093569 
boosting mean 284 : -0.4786122 
boosting RMSE 284 : 0.142729 

forest 284 : -0.4051603 
forest mean 284 : -0.389171 
forest RMSE 284 : 0.05066798 

nnet 284 : -0.4856816 
nnet mean 284 : -0.4083834 
nnet RMSE 284 : 0.1473356 


s: 285 
logit 285 : -0.5285983 
logit mean 285 : -0.4387324 
logit RMSE 285 : 0.06993256 

boosting 285 : -0.500065 
boosting mean 285 : -0.4786875 
boosting RMSE 285 : 0.1426016 

forest 285 : -0.4820216 
forest mean 285 : -0.3894968 
forest RMSE 285 : 0.05081182 

nnet 285 : -0.444228 
nnet mean 285 : -0.4085092 
nnet RMSE 285 : 0.1471002 


s: 286 
logit 286 : -0.4141107 
logit mean 286 : -0.4386463 
logit RMSE 286 : 0.06981518 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 286 : -0.4280962 
boosting mean 286 : -0.4785106 
boosting RMSE 286 : 0.1423618 

forest 286 : -0.356622 
forest mean 286 : -0.3893819 
forest RMSE 286 : 0.05078773 

nnet 286 : -0.3628392 
nnet mean 286 : -0.4083495 
nnet RMSE 286 : 0.1468593 


s: 287 
logit 287 : -0.4354842 
logit mean 287 : -0.4386353 
logit RMSE 287 : 0.06972491 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 287 : -0.4657424 
boosting mean 287 : -0.4784661 
boosting RMSE 287 : 0.1421665 

forest 287 : -0.3378581 
forest mean 287 : -0.3892023 
forest RMSE 287 : 0.05083169 

nnet 287 : -0.4289765 
nnet mean 287 : -0.4084214 
nnet RMSE 287 : 0.1466132 


s: 288 
logit 288 : -0.3882105 
logit mean 288 : -0.4384602 
logit RMSE 288 : 0.06960722 

boosting 288 : -0.393665 
boosting mean 288 : -0.4781716 
boosting RMSE 288 : 0.1419200 

forest 288 : -0.4172877 
forest mean 288 : -0.3892999 
forest RMSE 288 : 0.05075359 

nnet 288 : -0.3439093 
nnet mean 288 : -0.4081974 
nnet RMSE 288 : 0.1463957 


s: 289 
logit 289 : -0.4400972 
logit mean 289 : -0.4384659 
logit RMSE 289 : 0.06952671 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 289 : -0.4962762 
boosting mean 289 : -0.4782343 
boosting RMSE 289 : 0.1417874 

forest 289 : -0.3413349 
forest mean 289 : -0.3891339 
forest RMSE 289 : 0.05078309 

nnet 289 : -0.5457391 
nnet mean 289 : -0.4086733 
nnet RMSE 289 : 0.1463935 


s: 290 
logit 290 : -0.4145001 
logit mean 290 : -0.4383832 
logit RMSE 290 : 0.06941195 

boosting 290 : -0.4109394 
boosting mean 290 : -0.4780022 
boosting RMSE 290 : 0.1415442 

forest 290 : -0.4034402 
forest mean 290 : -0.3891832 
forest RMSE 290 : 0.05069586 

nnet 290 : -0.3179487 
nnet mean 290 : -0.4083604 
nnet RMSE 290 : 0.1462203 


s: 291 
logit 291 : -0.4276395 
logit mean 291 : -0.4383463 
logit RMSE 291 : 0.06931153 

boosting 291 : -0.5159808 
boosting mean 291 : -0.4781327 
boosting RMSE 291 : 0.1414642 

forest 291 : -0.4090366 
forest mean 291 : -0.3892514 
forest RMSE 291 : 0.05061145 

nnet 291 : -0.7282336 
nnet mean 291 : -0.4094597 
nnet RMSE 291 : 0.1472315 


s: 292 
logit 292 : -0.4257895 
logit mean 292 : -0.4383033 
logit RMSE 292 : 0.0692092 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 292 : -0.3238732 
boosting mean 292 : -0.4776044 
boosting RMSE 292 : 0.1412920 

forest 292 : -0.386136 
forest mean 292 : -0.3892408 
forest RMSE 292 : 0.05053123 

nnet 292 : -0.4594195 
nnet mean 292 : -0.4096307 
nnet RMSE 292 : 0.1470203 


s: 293 
logit 293 : -0.4523346 
logit mean 293 : -0.4383512 
logit RMSE 293 : 0.06915861 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 293 : -0.6126851 
boosting mean 293 : -0.4780655 
boosting RMSE 293 : 0.1415969 

forest 293 : -0.4802937 
forest mean 293 : -0.3895515 
forest RMSE 293 : 0.05066255 

nnet 293 : -0.2133875 
nnet mean 293 : -0.408961 
nnet RMSE 293 : 0.1471736 


s: 294 
logit 294 : -0.3939115 
logit mean 294 : -0.4382001 
logit RMSE 294 : 0.0690418 

boosting 294 : -0.4417061 
boosting mean 294 : -0.4779418 
boosting RMSE 294 : 0.1413768 

forest 294 : -0.3132959 
forest mean 294 : -0.3892922 
forest RMSE 294 : 0.05082847 

nnet 294 : -0.3925798 
nnet mean 294 : -0.4089053 
nnet RMSE 294 : 0.1469237 


s: 295 
logit 295 : -0.4465552 
logit mean 295 : -0.4382284 
logit RMSE 295 : 0.06897796 

boosting 295 : -0.641764 
boosting mean 295 : -0.4784971 
boosting RMSE 295 : 0.1418372 

forest 295 : -0.4843878 
forest mean 295 : -0.3896145 
forest RMSE 295 : 0.05097957 

nnet 295 : -0.4179401 
nnet mean 295 : -0.4089359 
nnet RMSE 295 : 0.1466782 


s: 296 
logit 296 : -0.4558139 
logit mean 296 : -0.4382878 
logit RMSE 296 : 0.06893772 

boosting 296 : -0.5418607 
boosting mean 296 : -0.4787112 
boosting RMSE 296 : 0.1418373 

forest 296 : -0.3654457 
forest mean 296 : -0.3895329 
forest RMSE 296 : 0.05093299 

nnet 296 : -0.3628591 
nnet mean 296 : -0.4087802 
nnet RMSE 296 : 0.1464461 


s: 297 
logit 297 : -0.5406529 
logit mean 297 : -0.4386325 
logit RMSE 297 : 0.06930381 

boosting 297 : -0.5010593 
boosting mean 297 : -0.4787864 
boosting RMSE 297 : 0.1417197 

forest 297 : -0.4753797 
forest mean 297 : -0.3898219 
forest RMSE 297 : 0.05103496 

nnet 297 : -0.2081015 
nnet mean 297 : -0.4081045 
nnet RMSE 297 : 0.1466228 


s: 298 
logit 298 : -0.6259386 
logit mean 298 : -0.439261 
logit RMSE 298 : 0.07041451 

boosting 298 : -0.45147 
boosting mean 298 : -0.4786948 
boosting RMSE 298 : 0.1415131 

forest 298 : -0.3885393 
forest mean 298 : -0.3898176 
forest RMSE 298 : 0.05095358 

nnet 298 : -0.511705 
nnet mean 298 : -0.4084522 
nnet RMSE 298 : 0.1465195 


s: 299 
logit 299 : -0.4664245 
logit mean 299 : -0.4393518 
logit RMSE 299 : 0.07040155 

boosting 299 : -0.2451313 
boosting mean 299 : -0.4779136 
boosting RMSE 299 : 0.1415599 

forest 299 : -0.3515263 
forest mean 299 : -0.3896895 
forest RMSE 299 : 0.05094549 

nnet 299 : -0.5154756 
nnet mean 299 : -0.4088101 
nnet RMSE 299 : 0.1464267 


s: 300 
logit 300 : -0.5597351 
logit mean 300 : -0.4397531 
logit RMSE 300 : 0.07088658 

boosting 300 : -0.5263912 
boosting mean 300 : -0.4780752 
boosting RMSE 300 : 0.141512 

forest 300 : -0.3920602 
forest mean 300 : -0.3896974 
forest RMSE 300 : 0.05086257 

nnet 300 : -0.3442438 
nnet mean 300 : -0.4085949 
nnet RMSE 300 : 0.1462179 


s: 301 
logit 301 : -0.3815972 
logit mean 301 : -0.4395599 
logit RMSE 301 : 0.07077668 

boosting 301 : -0.4629777 
boosting mean 301 : -0.4780251 
boosting RMSE 301 : 0.1413234 

forest 301 : -0.3208059 
forest mean 301 : -0.3894686 
forest RMSE 301 : 0.05098277 

nnet 301 : -0.5142045 
nnet mean 301 : -0.4089458 
nnet RMSE 301 : 0.1461231 


s: 302 
logit 302 : -0.366482 
logit mean 302 : -0.4393179 
logit RMSE 302 : 0.07068572 

boosting 302 : -0.4069105 
boosting mean 302 : -0.4777896 
boosting RMSE 302 : 0.1410898 

forest 302 : -0.385852 
forest mean 302 : -0.3894566 
forest RMSE 302 : 0.0509048 

nnet 302 : -0.5145009 
nnet mean 302 : -0.4092953 
nnet RMSE 302 : 0.1460297 


s: 303 
logit 303 : -0.4318807 
logit mean 303 : -0.4392934 
logit RMSE 303 : 0.07059275 

boosting 303 : -0.4337376 
boosting mean 303 : -0.4776442 
boosting RMSE 303 : 0.1408701 

forest 303 : -0.3648346 
forest mean 303 : -0.3893753 
forest RMSE 303 : 0.05086087 

nnet 303 : -0.4954485 
nnet mean 303 : -0.4095796 
nnet RMSE 303 : 0.1458916 


s: 304 
logit 304 : -0.3671731 
logit mean 304 : -0.4390562 
logit RMSE 304 : 0.07050169 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 304 : -0.4350113 
boosting mean 304 : -0.477504 
boosting RMSE 304 : 0.1406525 

forest 304 : -0.3994550 
forest mean 304 : -0.3894085 
forest RMSE 304 : 0.05077716 

nnet 304 : -0.4781196 
nnet mean 304 : -0.4098051 
nnet RMSE 304 : 0.1457204 


s: 305 
logit 305 : -0.3443470 
logit mean 305 : -0.4387456 
logit RMSE 305 : 0.07045812 

boosting 305 : -0.3661489 
boosting mean 305 : -0.4771389 
boosting RMSE 305 : 0.1404351 

forest 305 : -0.3693115 
forest mean 305 : -0.3893426 
forest RMSE 305 : 0.05072429 

nnet 305 : -0.6355812 
nnet mean 305 : -0.4105453 
nnet RMSE 305 : 0.1461053 


s: 306 
logit 306 : -0.5380116 
logit mean 306 : -0.43907 
logit RMSE 306 : 0.07078396 

boosting 306 : -0.4784526 
boosting mean 306 : -0.4771432 
boosting RMSE 306 : 0.1402772 

forest 306 : -0.4391211 
forest mean 306 : -0.3895053 
forest RMSE 306 : 0.0506907 

nnet 306 : -0.2654771 
nnet mean 306 : -0.4100712 
nnet RMSE 306 : 0.146069 


s: 307 
logit 307 : -0.4345079 
logit mean 307 : -0.4390552 
logit RMSE 307 : 0.07069602 

boosting 307 : -0.5211964 
boosting mean 307 : -0.4772867 
boosting RMSE 307 : 0.1402193 

forest 307 : -0.4683274 
forest mean 307 : -0.389762 
forest RMSE 307 : 0.0507581 

nnet 307 : -0.4712474 
nnet mean 307 : -0.4102705 
nnet RMSE 307 : 0.1458876 


s: 308 
logit 308 : -0.4610354 
logit mean 308 : -0.4391265 
logit RMSE 308 : 0.07066679 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 308 : -0.5181424 
boosting mean 308 : -0.4774193 
boosting RMSE 308 : 0.1401532 

forest 308 : -0.4338421 
forest mean 308 : -0.3899051 
forest RMSE 308 : 0.05071231 

nnet 308 : -0.4517042 
nnet mean 308 : -0.4104050 
nnet RMSE 308 : 0.1456803 


s: 309 
logit 309 : -0.5486516 
logit mean 309 : -0.439481 
logit RMSE 309 : 0.07105735 

boosting 309 : -0.391858 
boosting mean 309 : -0.4771424 
boosting RMSE 309 : 0.139927 

forest 309 : -0.3527907 
forest mean 309 : -0.389785 
forest RMSE 309 : 0.05070136 

nnet 309 : -0.2733789 
nnet mean 309 : -0.4099616 
nnet RMSE 309 : 0.1456227 


s: 310 
logit 310 : -0.4603007 
logit mean 310 : -0.4395481 
logit RMSE 310 : 0.07102527 

boosting 310 : -0.6273148 
boosting mean 310 : -0.4776268 
boosting RMSE 310 : 0.1402964 

forest 310 : -0.3891489 
forest mean 310 : -0.389783 
forest RMSE 310 : 0.05062327 

nnet 310 : -0.3936739 
nnet mean 310 : -0.4099091 
nnet RMSE 310 : 0.1453881 


s: 311 
logit 311 : -0.4248567 
logit mean 311 : -0.4395009 
logit RMSE 311 : 0.07092499 

boosting 311 : -0.7600084 
boosting mean 311 : -0.4785348 
boosting RMSE 311 : 0.1415505 

forest 311 : -0.3451847 
forest mean 311 : -0.3896396 
forest RMSE 311 : 0.05063731 

nnet 311 : -0.4546693 
nnet mean 311 : -0.410053 
nnet RMSE 311 : 0.1451872 


s: 312 
logit 312 : -0.4429147 
logit mean 312 : -0.4395118 
logit RMSE 312 : 0.0708529 

boosting 312 : -0.6092677 
boosting mean 312 : -0.4789538 
boosting RMSE 312 : 0.1418192 

forest 312 : -0.4818814 
forest mean 312 : -0.3899352 
forest RMSE 312 : 0.05076817 

nnet 312 : -0.5783563 
nnet mean 312 : -0.4105924 
nnet RMSE 312 : 0.1453056 


s: 313 
logit 313 : -0.4888967 
logit mean 313 : -0.4396696 
logit RMSE 313 : 0.07091787 

boosting 313 : -0.5137376 
boosting mean 313 : -0.479065 
boosting RMSE 313 : 0.1417383 

forest 313 : -0.4438945 
forest mean 313 : -0.3901076 
forest RMSE 313 : 0.0507477 

nnet 313 : -0.6407382 
nnet mean 313 : -0.4113277 
nnet RMSE 313 : 0.1457101 


s: 314 
logit 314 : -0.5161068 
logit mean 314 : -0.4399131 
logit RMSE 314 : 0.07110738 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 314 : -0.4510761 
boosting mean 314 : -0.4789758 
boosting RMSE 314 : 0.1415418 

forest 314 : -0.3622069 
forest mean 314 : -0.3900188 
forest RMSE 314 : 0.05071169 

nnet 314 : -0.4923486 
nnet mean 314 : -0.4115857 
nnet RMSE 314 : 0.1455712 


s: 315 
logit 315 : -0.513039 
logit mean 315 : -0.4401452 
logit RMSE 315 : 0.07127953 

boosting 315 : -0.5236539 
boosting mean 315 : -0.4791177 
boosting RMSE 315 : 0.1414886 

forest 315 : -0.3670524 
forest mean 315 : -0.3899459 
forest RMSE 315 : 0.05066515 

nnet 315 : -0.6115126 
nnet mean 315 : -0.4122204 
nnet RMSE 315 : 0.1458277 


s: 316 
logit 316 : -0.42024 
logit mean 316 : -0.4400822 
logit RMSE 316 : 0.07117577 

boosting 316 : -0.41211 
boosting mean 316 : -0.4789056 
boosting RMSE 316 : 0.1412662 

forest 316 : -0.3707655 
forest mean 316 : -0.3898852 
forest RMSE 316 : 0.05061165 

nnet 316 : -0.4866034 
nnet mean 316 : -0.4124558 
nnet RMSE 316 : 0.1456783 


s: 317 
logit 317 : -0.4543199 
logit mean 317 : -0.4401271 
logit RMSE 317 : 0.07112888 

boosting 317 : -0.4322741 
boosting mean 317 : -0.4787585 
boosting RMSE 317 : 0.1410548 

forest 317 : -0.4078033 
forest mean 317 : -0.3899417 
forest RMSE 317 : 0.05053366 

nnet 317 : -0.2153759 
nnet mean 317 : -0.4118341 
nnet RMSE 317 : 0.1458175 


s: 318 
logit 318 : -0.4478029 
logit mean 318 : -0.4401513 
logit RMSE 318 : 0.07106752 

boosting 318 : -0.3880877 
boosting mean 318 : -0.4784734 
boosting RMSE 318 : 0.1408345 

forest 318 : -0.3598893 
forest mean 318 : -0.3898472 
forest RMSE 318 : 0.05050425 

nnet 318 : -0.5065626 
nnet mean 318 : -0.412132 
nnet RMSE 318 : 0.1457106 


s: 319 
logit 319 : -0.4085906 
logit mean 319 : -0.4400523 
logit RMSE 319 : 0.07095768 

boosting 319 : -0.5742877 
boosting mean 319 : -0.4787737 
boosting RMSE 319 : 0.1409517 

forest 319 : -0.3275928 
forest mean 319 : -0.389652 
forest RMSE 319 : 0.05058773 

nnet 319 : -0.2618690 
nnet mean 319 : -0.4116609 
nnet RMSE 319 : 0.1456875 


s: 320 
logit 320 : -0.4177739 
logit mean 320 : -0.4399827 
logit RMSE 320 : 0.07085369 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 320 : -0.5817326 
boosting mean 320 : -0.4790955 
boosting RMSE 320 : 0.1410975 

forest 320 : -0.4555288 
forest mean 320 : -0.3898579 
forest RMSE 320 : 0.05060393 

nnet 320 : -0.7082835 
nnet mean 320 : -0.4125879 
nnet RMSE 320 : 0.1464770 


s: 321 
logit 321 : -0.4348803 
logit mean 321 : -0.4399668 
logit RMSE 321 : 0.07077002 

boosting 321 : -0.4187565 
boosting mean 321 : -0.4789075 
boosting RMSE 321 : 0.1408815 

forest 321 : -0.4021717 
forest mean 321 : -0.3898962 
forest RMSE 321 : 0.05052519 

nnet 321 : -0.285437 
nnet mean 321 : -0.4121918 
nnet RMSE 321 : 0.1463884 


s: 322 
logit 322 : -0.3134307 
logit mean 322 : -0.4395738 
logit RMSE 322 : 0.07082454 

boosting 322 : -0.3883788 
boosting mean 322 : -0.4786264 
boosting RMSE 322 : 0.1406640 

forest 322 : -0.3682798 
forest mean 322 : -0.3898291 
forest RMSE 322 : 0.05047763 

nnet 322 : -0.3904200 
nnet mean 322 : -0.4121242 
nnet RMSE 322 : 0.1461619 


s: 323 
logit 323 : -0.4873292 
logit mean 323 : -0.4397217 
logit RMSE 323 : 0.07088157 

boosting 323 : -0.5981352 
boosting mean 323 : -0.4789964 
boosting RMSE 323 : 0.1408782 

forest 323 : -0.4587288 
forest mean 323 : -0.3900424 
forest RMSE 323 : 0.05050526 

nnet 323 : -0.3442069 
nnet mean 323 : -0.4119139 
nnet RMSE 323 : 0.1459685 


s: 324 
logit 324 : -0.5008791 
logit mean 324 : -0.4399104 
logit RMSE 324 : 0.07099366 

boosting 324 : -0.5427143 
boosting mean 324 : -0.479193 
boosting RMSE 324 : 0.1408839 

forest 324 : -0.3939935 
forest mean 324 : -0.3900546 
forest RMSE 324 : 0.05042836 

nnet 324 : -0.4621989 
nnet mean 324 : -0.4120691 
nnet RMSE 324 : 0.145784 


s: 325 
logit 325 : -0.4119527 
logit mean 325 : -0.4398244 
logit RMSE 325 : 0.07088745 

boosting 325 : -0.4965547 
boosting mean 325 : -0.4792464 
boosting RMSE 325 : 0.1407689 

forest 325 : -0.4099093 
forest mean 325 : -0.3901157 
forest RMSE 325 : 0.05035372 

nnet 325 : -0.3303165 
nnet mean 325 : -0.4118176 
nnet RMSE 325 : 0.1456109 


s: 326 
logit 326 : -0.4076901 
logit mean 326 : -0.4397259 
logit RMSE 326 : 0.07077993 

boosting 326 : -0.4587554 
boosting mean 326 : -0.4791836 
boosting RMSE 326 : 0.1405905 

forest 326 : -0.2791795 
forest mean 326 : -0.3897754 
forest RMSE 326 : 0.05071979 

nnet 326 : 0.05868349 
nnet mean 326 : -0.4103743 
nnet RMSE 326 : 0.1475901 


s: 327 
logit 327 : -0.4442413 
logit mean 327 : -0.4397397 
logit RMSE 327 : 0.07071395 

boosting 327 : -0.4811159 
boosting mean 327 : -0.4791895 
boosting RMSE 327 : 0.140447 

forest 327 : -0.3453383 
forest mean 327 : -0.3896395 
forest RMSE 327 : 0.05073232 

nnet 327 : -0.3642961 
nnet mean 327 : -0.4102334 
nnet RMSE 327 : 0.1473775 


s: 328 
logit 328 : -0.3872262 
logit mean 328 : -0.4395796 
logit RMSE 328 : 0.0706096 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 328 : -0.5779693 
boosting mean 328 : -0.4794906 
boosting RMSE 328 : 0.1405766 

forest 328 : -0.3703886 
forest mean 328 : -0.3895808 
forest RMSE 328 : 0.0506813 

nnet 328 : -0.3922919 
nnet mean 328 : -0.4101787 
nnet RMSE 328 : 0.1471533 


s: 329 
logit 329 : -0.424985 
logit mean 329 : -0.4395352 
logit RMSE 329 : 0.07051566 

boosting 329 : -0.4908507 
boosting mean 329 : -0.4795252 
boosting RMSE 329 : 0.1404521 

forest 329 : -0.4329852 
forest mean 329 : -0.3897128 
forest RMSE 329 : 0.05063688 

nnet 329 : -0.3994706 
nnet mean 329 : -0.4101461 
nnet RMSE 329 : 0.1469295 


s: 330 
logit 330 : -0.4642049 
logit mean 330 : -0.4396100 
logit RMSE 330 : 0.07049739 

boosting 330 : -0.4592953 
boosting mean 330 : -0.4794639 
boosting RMSE 330 : 0.1402772 

forest 330 : -0.3405746 
forest mean 330 : -0.3895639 
forest RMSE 330 : 0.05066582 

nnet 330 : -0.5806669 
nnet mean 330 : -0.4106629 
nnet RMSE 330 : 0.1470434 


s: 331 
logit 331 : -0.478805 
logit mean 331 : -0.4397284 
logit RMSE 331 : 0.07052396 

boosting 331 : -0.7350411 
boosting mean 331 : -0.480236 
boosting RMSE 331 : 0.1412705 

forest 331 : -0.4859383 
forest mean 331 : -0.389855 
forest RMSE 331 : 0.05080927 

nnet 331 : -0.5546959 
nnet mean 331 : -0.411098 
nnet RMSE 331 : 0.1470672 


s: 332 
logit 332 : -0.4234716 
logit mean 332 : -0.4396794 
logit RMSE 332 : 0.07042945 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 332 : -0.446867 
boosting mean 332 : -0.4801355 
boosting RMSE 332 : 0.1410811 

forest 332 : -0.3519095 
forest mean 332 : -0.3897407 
forest RMSE 332 : 0.0508013 

nnet 332 : -0.5853614 
nnet mean 332 : -0.4116229 
nnet RMSE 332 : 0.1471975 


s: 333 
logit 333 : -0.5687386 
logit mean 333 : -0.440067 
logit RMSE 333 : 0.07092895 

boosting 333 : -0.2786215 
boosting mean 333 : -0.4795304 
boosting RMSE 333 : 0.1410260 

forest 333 : -0.3562705 
forest mean 333 : -0.3896402 
forest RMSE 333 : 0.05078154 

nnet 333 : -0.5755306 
nnet mean 333 : -0.4121151 
nnet RMSE 333 : 0.1472907 


s: 334 
logit 334 : -0.4261571 
logit mean 334 : -0.4400253 
logit RMSE 334 : 0.07083715 

boosting 334 : -0.4342925 
boosting mean 334 : -0.4793949 
boosting RMSE 334 : 0.1408272 

forest 334 : -0.2952216 
forest mean 334 : -0.3893575 
forest RMSE 334 : 0.05102856 

nnet 334 : -0.3478001 
nnet mean 334 : -0.4119226 
nnet RMSE 334 : 0.1470978 


s: 335 
logit 335 : -0.4415636 
logit mean 335 : -0.4400299 
logit RMSE 335 : 0.07076779 

boosting 335 : -0.6371018 
boosting mean 335 : -0.4798657 
boosting RMSE 335 : 0.1412123 

forest 335 : -0.3750329 
forest mean 335 : -0.3893148 
forest RMSE 335 : 0.0509706 

nnet 335 : -0.4396384 
nnet mean 335 : -0.4120053 
nnet RMSE 335 : 0.1468940 


s: 336 
logit 336 : -0.5738297 
logit mean 336 : -0.4404281 
logit RMSE 336 : 0.0712959 

boosting 336 : -0.5512052 
boosting mean 336 : -0.480078 
boosting RMSE 336 : 0.1412431 

forest 336 : -0.4894854 
forest mean 336 : -0.3896129 
forest RMSE 336 : 0.05112829 

nnet 336 : -0.7343376 
nnet mean 336 : -0.4129646 
nnet RMSE 336 : 0.147805 


s: 337 
logit 337 : -0.4485662 
logit mean 337 : -0.4404523 
logit RMSE 337 : 0.07123918 

boosting 337 : -0.5067494 
boosting mean 337 : -0.4801571 
boosting RMSE 337 : 0.1411532 

forest 337 : -0.4371569 
forest mean 337 : -0.389754 
forest RMSE 337 : 0.05109248 

nnet 337 : -0.4835038 
nnet mean 337 : -0.4131739 
nnet RMSE 337 : 0.1476556 


s: 338 
logit 338 : -0.4264135 
logit mean 338 : -0.4404107 
logit RMSE 338 : 0.07114823 

boosting 338 : -0.570349 
boosting mean 338 : -0.480424 
boosting RMSE 338 : 0.1412485 

forest 338 : -0.3812727 
forest mean 338 : -0.3897289 
forest RMSE 338 : 0.05102701 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 338 : -0.2527698 
nnet mean 338 : -0.4126994 
nnet RMSE 338 : 0.1476544 


s: 339 
logit 339 : -0.3743341 
logit mean 339 : -0.4402158 
logit RMSE 339 : 0.07105689 

boosting 339 : -0.4174481 
boosting mean 339 : -0.4802382 
boosting RMSE 339 : 0.1410432 

forest 339 : -0.4770036 
forest mean 339 : -0.3899863 
forest RMSE 339 : 0.05112305 

nnet 339 : -0.4815648 
nnet mean 339 : -0.4129025 
nnet RMSE 339 : 0.1475030 


s: 340 
logit 340 : -0.6163793 
logit mean 340 : -0.4407340 
logit RMSE 340 : 0.07191618 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 340 : -0.2489474 
boosting mean 340 : -0.4795579 
boosting RMSE 340 : 0.1410737 

forest 340 : -0.426174 
forest mean 340 : -0.3900928 
forest RMSE 340 : 0.05106755 

nnet 340 : -0.2894345 
nnet mean 340 : -0.4125394 
nnet RMSE 340 : 0.1474079 


s: 341 
logit 341 : -0.5181006 
logit mean 341 : -0.4409608 
logit RMSE 341 : 0.07209489 

boosting 341 : -0.6017314 
boosting mean 341 : -0.4799162 
boosting RMSE 341 : 0.1412897 

forest 341 : -0.4811619 
forest mean 341 : -0.3903598 
forest RMSE 341 : 0.05118168 

nnet 341 : -0.2260800 
nnet mean 341 : -0.4119926 
nnet RMSE 341 : 0.1474926 


s: 342 
logit 342 : -0.3901381 
logit mean 342 : -0.4408122 
logit RMSE 342 : 0.07199138 

boosting 342 : -0.4838769 
boosting mean 342 : -0.4799278 
boosting RMSE 342 : 0.1411558 

forest 342 : -0.3394361 
forest mean 342 : -0.3902109 
forest RMSE 342 : 0.05121162 

nnet 342 : -0.4180785 
nnet mean 342 : -0.4120104 
nnet RMSE 342 : 0.1472801 


s: 343 
logit 343 : -0.4925093 
logit mean 343 : -0.4409630 
logit RMSE 343 : 0.0720597 

boosting 343 : -0.496901 
boosting mean 343 : -0.4799773 
boosting RMSE 343 : 0.141047 

forest 343 : -0.3076805 
forest mean 343 : -0.3899703 
forest RMSE 343 : 0.05137929 

nnet 343 : -0.6303166 
nnet mean 343 : -0.4126468 
nnet RMSE 343 : 0.1475901 


s: 344 
logit 344 : -0.4618941 
logit mean 344 : -0.4410238 
logit RMSE 344 : 0.07203222 

boosting 344 : -0.5380658 
boosting mean 344 : -0.4801462 
boosting RMSE 344 : 0.1410384 

forest 344 : -0.4054626 
forest mean 344 : -0.3900153 
forest RMSE 344 : 0.05130541 

nnet 344 : -0.53121 
nnet mean 344 : -0.4129915 
nnet RMSE 344 : 0.1475451 


s: 345 
logit 345 : -0.3209280 
logit mean 345 : -0.4406757 
logit RMSE 345 : 0.07205362 

boosting 345 : -0.4427522 
boosting mean 345 : -0.4800378 
boosting RMSE 345 : 0.1408527 

forest 345 : -0.2510634 
forest mean 345 : -0.3896126 
forest RMSE 345 : 0.05185471 

nnet 345 : -0.4338028 
nnet mean 345 : -0.4130518 
nnet RMSE 345 : 0.1473423 


s: 346 
logit 346 : -0.3783337 
logit mean 346 : -0.4404955 
logit RMSE 346 : 0.07195885 

boosting 346 : -0.4859712 
boosting mean 346 : -0.4800549 
boosting RMSE 346 : 0.1407249 

forest 346 : -0.2811315 
forest mean 346 : -0.3892991 
forest RMSE 346 : 0.05217257 

nnet 346 : -0.2642134 
nnet mean 346 : -0.4126216 
nnet RMSE 346 : 0.1473103 


s: 347 
logit 347 : -0.5970023 
logit mean 347 : -0.4409465 
logit RMSE 347 : 0.07262918 

boosting 347 : -0.6090103 
boosting mean 347 : -0.4804265 
boosting RMSE 347 : 0.1409692 

forest 347 : -0.5116302 
forest mean 347 : -0.3896516 
forest RMSE 347 : 0.05244086 

nnet 347 : -0.6619523 
nnet mean 347 : -0.4133402 
nnet RMSE 347 : 0.1477685 


s: 348 
logit 348 : -0.4045927 
logit mean 348 : -0.4408421 
logit RMSE 348 : 0.07252517 

boosting 348 : -0.4930171 
boosting mean 348 : -0.4804627 
boosting RMSE 348 : 0.1408548 

forest 348 : -0.4009374 
forest mean 348 : -0.389684 
forest RMSE 348 : 0.05236549 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 348 : -0.2227351 
nnet mean 348 : -0.4127925 
nnet RMSE 348 : 0.1478617 


s: 349 
logit 349 : -0.4899491 
logit mean 349 : -0.4409828 
logit RMSE 349 : 0.07258107 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 349 : -0.3958609 
boosting mean 349 : -0.4802203 
boosting RMSE 349 : 0.1406530 

forest 349 : -0.3923451 
forest mean 349 : -0.3896917 
forest RMSE 349 : 0.05229202 

nnet 349 : -0.4859614 
nnet mean 349 : -0.4130021 
nnet RMSE 349 : 0.1477214 


s: 350 
logit 350 : -0.4353983 
logit mean 350 : -0.4409668 
logit RMSE 350 : 0.072502 

boosting 350 : -0.3788507 
boosting mean 350 : -0.4799307 
boosting RMSE 350 : 0.1404565 

forest 350 : -0.5033898 
forest mean 350 : -0.3900165 
forest RMSE 350 : 0.05250889 

nnet 350 : -0.7339376 
nnet mean 350 : -0.4139191 
nnet RMSE 350 : 0.1485862 


s: 351 
logit 351 : -0.4383184 
logit mean 351 : -0.4409593 
logit RMSE 351 : 0.07242753 

boosting 351 : -0.4787255 
boosting mean 351 : -0.4799273 
boosting RMSE 351 : 0.1403192 

forest 351 : -0.4230127 
forest mean 351 : -0.3901105 
forest RMSE 351 : 0.05244842 

nnet 351 : -0.2076195 
nnet mean 351 : -0.4133313 
nnet RMSE 351 : 0.1487293 


s: 352 
logit 352 : -0.437008 
logit mean 352 : -0.4409481 
logit RMSE 352 : 0.07235147 

boosting 352 : -0.403578 
boosting mean 352 : -0.4797103 
boosting RMSE 352 : 0.1401199 

forest 352 : -0.3880453 
forest mean 352 : -0.3901046 
forest RMSE 352 : 0.05237774 

nnet 352 : -0.2507899 
nnet mean 352 : -0.4128695 
nnet RMSE 352 : 0.1487307 


s: 353 
logit 353 : -0.4139096 
logit mean 353 : -0.4408715 
logit RMSE 353 : 0.07225271 

boosting 353 : -0.5791575 
boosting mean 353 : -0.4799921 
boosting RMSE 353 : 0.1402458 

forest 353 : -0.3688323 
forest mean 353 : -0.3900444 
forest RMSE 353 : 0.0523298 

nnet 353 : -0.3983061 
nnet mean 353 : -0.4128283 
nnet RMSE 353 : 0.1485199 


s: 354 
logit 354 : -0.4418107 
logit mean 354 : -0.4408741 
logit RMSE 354 : 0.0721848 

boosting 354 : -0.6315629 
boosting mean 354 : -0.4804202 
boosting RMSE 354 : 0.1405874 

forest 354 : -0.3443395 
forest mean 354 : -0.3899153 
forest RMSE 354 : 0.05233951 

nnet 354 : -0.4797583 
nnet mean 354 : -0.4130174 
nnet RMSE 354 : 0.1483705 


s: 355 
logit 355 : -0.4417947 
logit mean 355 : -0.4408767 
logit RMSE 355 : 0.07211719 

boosting 355 : -0.3359637 
boosting mean 355 : -0.4800133 
boosting RMSE 355 : 0.1404303 

forest 355 : -0.3796478 
forest mean 355 : -0.3898863 
forest RMSE 355 : 0.0522769 

nnet 355 : -0.3629368 
nnet mean 355 : -0.4128763 
nnet RMSE 355 : 0.1481745 


s: 356 
logit 356 : -0.5265952 
logit mean 356 : -0.4411175 
logit RMSE 356 : 0.0723277 

boosting 356 : -0.5132122 
boosting mean 356 : -0.4801066 
boosting RMSE 356 : 0.1403613 

forest 356 : -0.4346012 
forest mean 356 : -0.3900120 
forest RMSE 356 : 0.05223563 

nnet 356 : -0.4288805 
nnet mean 356 : -0.4129212 
nnet RMSE 356 : 0.1479741 


s: 357 
logit 357 : -0.4436455 
logit mean 357 : -0.4411246 
logit RMSE 357 : 0.07226327 

boosting 357 : -0.5614743 
boosting mean 357 : -0.4803345 
boosting RMSE 357 : 0.1404249 

forest 357 : -0.4238228 
forest mean 357 : -0.3901067 
forest RMSE 357 : 0.05217765 

nnet 357 : -0.5020841 
nnet mean 357 : -0.413171 
nnet RMSE 357 : 0.1478655 


s: 358 
logit 358 : -0.488889 
logit mean 358 : -0.441258 
logit RMSE 358 : 0.07231503 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 358 : -0.5485814 
boosting mean 358 : -0.4805251 
boosting RMSE 358 : 0.1404483 

forest 358 : -0.380636 
forest mean 358 : -0.3900802 
forest RMSE 358 : 0.05211478 

nnet 358 : -0.4352079 
nnet mean 358 : -0.4132326 
nnet RMSE 358 : 0.1476705 


s: 359 
logit 359 : -0.533115 
logit mean 359 : -0.4415139 
logit RMSE 359 : 0.07255519 

boosting 359 : -0.630336 
boosting mean 359 : -0.4809424 
boosting RMSE 359 : 0.1407784 

forest 359 : -0.4206486 
forest mean 359 : -0.3901654 
forest RMSE 359 : 0.05205355 

nnet 359 : -0.231199 
nnet mean 359 : -0.4127255 
nnet RMSE 359 : 0.1477336 


s: 360 
logit 360 : -0.5076897 
logit mean 360 : -0.4416977 
logit RMSE 360 : 0.07267631 

boosting 360 : -0.5649267 
boosting mean 360 : -0.4811757 
boosting RMSE 360 : 0.1408512 

forest 360 : -0.4613173 
forest mean 360 : -0.390363 
forest RMSE 360 : 0.05208157 

nnet 360 : -0.66578 
nnet mean 360 : -0.4134284 
nnet RMSE 360 : 0.1481918 


s: 361 
logit 361 : -0.5011471 
logit mean 361 : -0.4418624 
logit RMSE 361 : 0.07277056 

boosting 361 : -0.5381577 
boosting mean 361 : -0.4813336 
boosting RMSE 361 : 0.1408438 

forest 361 : -0.3713983 
forest mean 361 : -0.3903105 
forest RMSE 361 : 0.05203116 

nnet 361 : -0.2448204 
nnet mean 361 : -0.4129614 
nnet RMSE 361 : 0.1482116 


s: 362 
logit 362 : -0.4132174 
logit mean 362 : -0.4417832 
logit RMSE 362 : 0.0726733 

boosting 362 : -0.6359337 
boosting mean 362 : -0.4817606 
boosting RMSE 362 : 0.1411948 

forest 362 : -0.3631457 
forest mean 362 : -0.3902354 
forest RMSE 362 : 0.05199534 

nnet 362 : -0.4963877 
nnet mean 362 : -0.4131918 
nnet RMSE 362 : 0.1480934 


s: 363 
logit 363 : -0.4048547 
logit mean 363 : -0.4416815 
logit RMSE 363 : 0.07257358 

boosting 363 : -0.3991521 
boosting mean 363 : -0.4815331 
boosting RMSE 363 : 0.1410001 

forest 363 : -0.4254909 
forest mean 363 : -0.3903325 
forest RMSE 363 : 0.05194091 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 363 : -0.2767024 
nnet mean 363 : -0.4128158 
nnet RMSE 363 : 0.1480308 


s: 364 
logit 364 : -0.4062183 
logit mean 364 : -0.4415841 
logit RMSE 364 : 0.07247456 

boosting 364 : -0.5049385 
boosting mean 364 : -0.4815974 
boosting RMSE 364 : 0.1409137 

forest 364 : -0.4138599 
forest mean 364 : -0.3903972 
forest RMSE 364 : 0.0518746 

nnet 364 : -0.8099781 
nnet mean 364 : -0.4139069 
nnet RMSE 364 : 0.1493810 


s: 365 
logit 365 : -0.4236747 
logit mean 365 : -0.441535 
logit RMSE 365 : 0.07238582 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 365 : -0.5379727 
boosting mean 365 : -0.4817518 
boosting RMSE 365 : 0.1409057 

forest 365 : -0.3399675 
forest mean 365 : -0.390259 
forest RMSE 365 : 0.0518987 

nnet 365 : -0.2893348 
nnet mean 365 : -0.4135656 
nnet RMSE 365 : 0.1492887 


s: 366 
logit 366 : -0.3816586 
logit mean 366 : -0.4413714 
logit RMSE 366 : 0.07229322 

boosting 366 : -0.4971323 
boosting mean 366 : -0.4817938 
boosting RMSE 366 : 0.1408047 

forest 366 : -0.3694394 
forest mean 366 : -0.3902021 
forest RMSE 366 : 0.05185236 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 366 : -0.160915 
nnet mean 366 : -0.4128753 
nnet RMSE 366 : 0.1496075 


s: 367 
logit 367 : -0.3843586 
logit mean 367 : -0.4412161 
logit RMSE 367 : 0.07219927 

boosting 367 : -0.4117196 
boosting mean 367 : -0.4816029 
boosting RMSE 367 : 0.1406140 

forest 367 : -0.3865922 
forest mean 367 : -0.3901923 
forest RMSE 367 : 0.0517864 

nnet 367 : -0.3791871 
nnet mean 367 : -0.4127835 
nnet RMSE 367 : 0.1494075 


s: 368 
logit 368 : -0.4215221 
logit mean 368 : -0.4411625 
logit RMSE 368 : 0.07210984 

boosting 368 : -0.5027736 
boosting mean 368 : -0.4816604 
boosting RMSE 368 : 0.1405250 

forest 368 : -0.3956483 
forest mean 368 : -0.3902071 
forest RMSE 368 : 0.05171649 

nnet 368 : -0.3897973 
nnet mean 368 : -0.4127211 
nnet RMSE 368 : 0.1492053 


s: 369 
logit 369 : -0.3678366 
logit mean 369 : -0.4409638 
logit RMSE 369 : 0.07203153 

boosting 369 : -0.3995341 
boosting mean 369 : -0.4814379 
boosting RMSE 369 : 0.1403345 

forest 369 : -0.3981234 
forest mean 369 : -0.3902286 
forest RMSE 369 : 0.05164646 

nnet 369 : -0.3630621 
nnet mean 369 : -0.4125865 
nnet RMSE 369 : 0.1490154 


s: 370 
logit 370 : -0.4745198 
logit mean 370 : -0.4410545 
logit RMSE 370 : 0.07203837 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 370 : -0.3696992 
boosting mean 370 : -0.4811359 
boosting RMSE 370 : 0.1401536 

forest 370 : -0.3396774 
forest mean 370 : -0.3900920 
forest RMSE 370 : 0.05167187 

nnet 370 : -0.4471386 
nnet mean 370 : -0.4126799 
nnet RMSE 370 : 0.1488340 


s: 371 
logit 371 : -0.4736721 
logit mean 371 : -0.4411424 
logit RMSE 371 : 0.07204282 

boosting 371 : -0.7245992 
boosting mean 371 : -0.4817921 
boosting RMSE 371 : 0.1409755 

forest 371 : -0.3761003 
forest mean 371 : -0.3900542 
forest RMSE 371 : 0.0516171 

nnet 371 : -0.4726716 
nnet mean 371 : -0.4128416 
nnet RMSE 371 : 0.1486812 


s: 372 
logit 372 : -0.4182281 
logit mean 372 : -0.4410808 
logit RMSE 372 : 0.07195213 

boosting 372 : -0.637169 
boosting mean 372 : -0.4822098 
boosting RMSE 372 : 0.1413218 

forest 372 : -0.3404703 
forest mean 372 : -0.3899209 
forest RMSE 372 : 0.05164 

nnet 372 : -0.3997329 
nnet mean 372 : -0.4128063 
nnet RMSE 372 : 0.1484812 


s: 373 
logit 373 : -0.4289041 
logit mean 373 : -0.4410482 
logit RMSE 373 : 0.0718712 

boosting 373 : -0.4362960 
boosting mean 373 : -0.4820867 
boosting RMSE 373 : 0.1411448 

forest 373 : -0.4301806 
forest mean 373 : -0.3900289 
forest RMSE 373 : 0.0515944 

nnet 373 : -0.633062 
nnet mean 373 : -0.4133968 
nnet RMSE 373 : 0.1487723 


s: 374 
logit 374 : -0.3833004 
logit mean 374 : -0.4408938 
logit RMSE 374 : 0.07178024 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 374 : -0.6175594 
boosting mean 374 : -0.4824489 
boosting RMSE 374 : 0.1414042 

forest 374 : -0.441238 
forest mean 374 : -0.3901658 
forest RMSE 374 : 0.05156948 

nnet 374 : -0.3567487 
nnet mean 374 : -0.4132454 
nnet RMSE 374 : 0.1485901 


s: 375 
logit 375 : -0.4864824 
logit mean 375 : -0.4410154 
logit RMSE 375 : 0.07182345 

boosting 375 : -0.6069162 
boosting mean 375 : -0.4827808 
boosting RMSE 375 : 0.1416192 

forest 375 : -0.3386449 
forest mean 375 : -0.3900284 
forest RMSE 375 : 0.05159804 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 375 : -0.2405961 
nnet mean 375 : -0.412785 
nnet RMSE 375 : 0.1486200 


s: 376 
logit 376 : -0.4425598 
logit mean 376 : -0.4410195 
logit RMSE 376 : 0.07176145 

boosting 376 : -0.4200823 
boosting mean 376 : -0.4826141 
boosting RMSE 376 : 0.1414345 

forest 376 : -0.3578889 
forest mean 376 : -0.3899429 
forest RMSE 376 : 0.05157513 

nnet 376 : -0.6153943 
nnet mean 376 : -0.4133238 
nnet RMSE 376 : 0.1488373 


s: 377 
logit 377 : -0.4542993 
logit mean 377 : -0.4410547 
logit RMSE 377 : 0.07172076 

boosting 377 : -0.4529416 
boosting mean 377 : -0.4825354 
boosting RMSE 377 : 0.1412731 

forest 377 : -0.3544295 
forest mean 377 : -0.3898487 
forest RMSE 377 : 0.05156012 

nnet 377 : -0.522088 
nnet mean 377 : -0.4136123 
nnet RMSE 377 : 0.1487727 


s: 378 
logit 378 : -0.4244596 
logit mean 378 : -0.4410108 
logit RMSE 378 : 0.07163687 

boosting 378 : -0.3761791 
boosting mean 378 : -0.482254 
boosting RMSE 378 : 0.1410915 

forest 378 : -0.4404849 
forest mean 378 : -0.3899827 
forest RMSE 378 : 0.05153396 

nnet 378 : -0.4681438 
nnet mean 378 : -0.4137566 
nnet RMSE 378 : 0.1486171 


s: 379 
logit 379 : -0.3780948 
logit mean 379 : -0.4408448 
logit RMSE 379 : 0.07155115 

boosting 379 : -0.2806074 
boosting mean 379 : -0.481722 
boosting RMSE 379 : 0.1410386 

forest 379 : -0.3876627 
forest mean 379 : -0.3899766 
forest RMSE 379 : 0.05146983 

nnet 379 : -0.5828403 
nnet mean 379 : -0.4142027 
nnet RMSE 379 : 0.1487178 


s: 380 
logit 380 : -0.4199944 
logit mean 380 : -0.4407899 
logit RMSE 380 : 0.0714643 

boosting 380 : -0.3298872 
boosting mean 380 : -0.4813224 
boosting RMSE 380 : 0.1408988 

forest 380 : -0.4190025 
forest mean 380 : -0.3900530 
forest RMSE 380 : 0.05141131 

nnet 380 : -0.2805268 
nnet mean 380 : -0.4138510 
nnet RMSE 380 : 0.1486484 


s: 381 
logit 381 : -0.4466761 
logit mean 381 : -0.4408054 
logit RMSE 381 : 0.0714105 

boosting 381 : -0.642733 
boosting mean 381 : -0.481746 
boosting RMSE 381 : 0.1412622 

forest 381 : -0.3690566 
forest mean 381 : -0.3899979 
forest RMSE 381 : 0.05136826 

nnet 381 : -0.2162727 
nnet mean 381 : -0.4133324 
nnet RMSE 381 : 0.1487513 


s: 382 
logit 382 : -0.437567 
logit mean 382 : -0.4407969 
logit RMSE 382 : 0.07134287 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 382 : -0.4539045 
boosting mean 382 : -0.4816732 
boosting RMSE 382 : 0.1411041 

forest 382 : -0.367419 
forest mean 382 : -0.3899387 
forest RMSE 382 : 0.05132806 

nnet 382 : -0.447474 
nnet mean 382 : -0.4134218 
nnet RMSE 382 : 0.1485763 


s: 383 
logit 383 : -0.468511 
logit mean 383 : -0.4408693 
logit RMSE 383 : 0.07133562 

boosting 383 : -0.5215507 
boosting mean 383 : -0.4817773 
boosting RMSE 383 : 0.1410566 

forest 383 : -0.4073872 
forest mean 383 : -0.3899843 
forest RMSE 383 : 0.0512624 

nnet 383 : -0.2958919 
nnet mean 383 : -0.4131149 
nnet RMSE 383 : 0.1484775 


s: 384 
logit 384 : -0.3619074 
logit mean 384 : -0.4406636 
logit RMSE 384 : 0.0712692 

boosting 384 : -0.4200678 
boosting mean 384 : -0.4816166 
boosting RMSE 384 : 0.1408766 

forest 384 : -0.3616166 
forest mean 384 : -0.3899104 
forest RMSE 384 : 0.05123306 

nnet 384 : -0.2994011 
nnet mean 384 : -0.4128188 
nnet RMSE 384 : 0.1483729 


s: 385 
logit 385 : -0.4257408 
logit mean 385 : -0.4406249 
logit RMSE 385 : 0.07118867 

boosting 385 : -0.5895059 
boosting mean 385 : -0.4818968 
boosting RMSE 385 : 0.1410246 

forest 385 : -0.4967905 
forest mean 385 : -0.390188 
forest RMSE 385 : 0.05140372 

nnet 385 : -0.732684 
nnet mean 385 : -0.4136496 
nnet RMSE 385 : 0.1491470 


s: 386 
logit 386 : -0.4677089 
logit mean 386 : -0.440695 
logit RMSE 386 : 0.07117987 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 386 : -0.5972834 
boosting mean 386 : -0.4821957 
boosting RMSE 386 : 0.1411993 

forest 386 : -0.3803087 
forest mean 386 : -0.3901624 
forest RMSE 386 : 0.05134688 

nnet 386 : -0.6847629 
nnet mean 386 : -0.4143519 
nnet RMSE 386 : 0.1496572 


s: 387 
logit 387 : -0.5087751 
logit mean 387 : -0.4408709 
logit RMSE 387 : 0.07130257 

boosting 387 : -0.5184016 
boosting mean 387 : -0.4822893 
boosting RMSE 387 : 0.1411451 

forest 387 : -0.4604085 
forest mean 387 : -0.3903440 
forest RMSE 387 : 0.05137235 

nnet 387 : -0.5321987 
nnet mean 387 : -0.4146565 
nnet RMSE 387 : 0.1496147 


s: 388 
logit 388 : -0.3698316 
logit mean 388 : -0.4406879 
logit RMSE 388 : 0.07122709 

boosting 388 : -0.3193028 
boosting mean 388 : -0.4818692 
boosting RMSE 388 : 0.1410227 

forest 388 : -0.3247509 
forest mean 388 : -0.3901749 
forest RMSE 388 : 0.05144813 

nnet 388 : -0.4926698 
nnet mean 388 : -0.4148575 
nnet RMSE 388 : 0.1494958 


s: 389 
logit 389 : -0.418038 
logit mean 389 : -0.4406296 
logit RMSE 389 : 0.07114136 

boosting 389 : -0.5045282 
boosting mean 389 : -0.4819275 
boosting RMSE 389 : 0.1409410 

forest 389 : -0.3728348 
forest mean 389 : -0.3901303 
forest RMSE 389 : 0.05140042 

nnet 389 : -0.5225149 
nnet mean 389 : -0.4151343 
nnet RMSE 389 : 0.1494327 


s: 390 
logit 390 : -0.4614944 
logit mean 390 : -0.4406831 
logit RMSE 390 : 0.0711183 

boosting 390 : -0.488 
boosting mean 390 : -0.481943 
boosting RMSE 390 : 0.1408307 

forest 390 : -0.4057851 
forest mean 390 : -0.3901705 
forest RMSE 390 : 0.05133531 

nnet 390 : -0.3423995 
nnet mean 390 : -0.4149478 
nnet RMSE 390 : 0.1492695 


s: 391 
logit 391 : -0.3003005 
logit mean 391 : -0.4403241 
logit RMSE 391 : 0.07120603 

boosting 391 : -0.3342954 
boosting mean 391 : -0.4815654 
boosting RMSE 391 : 0.1406897 

forest 391 : -0.3224891 
forest mean 391 : -0.3899974 
forest RMSE 391 : 0.05141926 

nnet 391 : -0.6454004 
nnet mean 391 : -0.4155372 
nnet RMSE 391 : 0.1495941 


s: 392 
logit 392 : -0.4860759 
logit mean 392 : -0.4404408 
logit RMSE 392 : 0.07124791 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 392 : -0.4783703 
boosting mean 392 : -0.4815573 
boosting RMSE 392 : 0.1405659 

forest 392 : -0.3988518 
forest mean 392 : -0.3900200 
forest RMSE 392 : 0.05135366 

nnet 392 : -0.8097607 
nnet mean 392 : -0.4165428 
nnet RMSE 392 : 0.1508299 


s: 393 
logit 393 : -0.4971989 
logit mean 393 : -0.4405852 
logit RMSE 393 : 0.07132593 

boosting 393 : -0.4545576 
boosting mean 393 : -0.4814886 
boosting RMSE 393 : 0.1404139 

forest 393 : -0.3992453 
forest mean 393 : -0.3900434 
forest RMSE 393 : 0.0512883 

nnet 393 : -0.3896980 
nnet mean 393 : -0.4164745 
nnet RMSE 393 : 0.1506387 


s: 394 
logit 394 : -0.4403551 
logit mean 394 : -0.4405846 
logit RMSE 394 : 0.07126436 

boosting 394 : -0.3497222 
boosting mean 394 : -0.4811541 
boosting RMSE 394 : 0.1402585 

forest 394 : -0.3838121 
forest mean 394 : -0.3900276 
forest RMSE 394 : 0.05122966 

nnet 394 : -0.3502718 
nnet mean 394 : -0.4163065 
nnet RMSE 394 : 0.1504683 


s: 395 
logit 395 : -0.4712515 
logit mean 395 : -0.4406623 
logit RMSE 395 : 0.07126433 

boosting 395 : -0.6409839 
boosting mean 395 : -0.4815588 
boosting RMSE 395 : 0.1406046 

forest 395 : -0.42576 
forest mean 395 : -0.3901181 
forest RMSE 395 : 0.05118119 

nnet 395 : -0.4290963 
nnet mean 395 : -0.4163389 
nnet RMSE 395 : 0.1502849 


s: 396 
logit 396 : -0.528174 
logit mean 396 : -0.4408833 
logit RMSE 396 : 0.07146514 

boosting 396 : -0.4224001 
boosting mean 396 : -0.4814094 
boosting RMSE 396 : 0.1404315 

forest 396 : -0.4237764 
forest mean 396 : -0.3902031 
forest RMSE 396 : 0.05113049 

nnet 396 : -0.4938706 
nnet mean 396 : -0.4165347 
nnet RMSE 396 : 0.1501691 


s: 397 
logit 397 : -0.5146828 
logit mean 397 : -0.4410692 
logit RMSE 397 : 0.07160677 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 397 : -0.4455124 
boosting mean 397 : -0.481319 
boosting RMSE 397 : 0.1402731 

forest 397 : -0.4178371 
forest mean 397 : -0.3902727 
forest RMSE 397 : 0.0510739 

nnet 397 : -0.4557718 
nnet mean 397 : -0.4166335 
nnet RMSE 397 : 0.1500060 


s: 398 
logit 398 : -0.3447819 
logit mean 398 : -0.4408272 
logit RMSE 398 : 0.0715703 

boosting 398 : -0.4800268 
boosting mean 398 : -0.4813157 
boosting RMSE 398 : 0.1401542 

forest 398 : -0.4248296 
forest mean 398 : -0.3903595 
forest RMSE 398 : 0.05102488 

nnet 398 : -0.2799459 
nnet mean 398 : -0.4162901 
nnet RMSE 398 : 0.1499382 


s: 399 
logit 399 : -0.3684538 
logit mean 399 : -0.4406458 
logit RMSE 399 : 0.071498 

boosting 399 : -0.4728699 
boosting mean 399 : -0.4812945 
boosting RMSE 399 : 0.1400260 

forest 399 : -0.4069112 
forest mean 399 : -0.390401 
forest RMSE 399 : 0.05096207 

nnet 399 : -0.539353 
nnet mean 399 : -0.4165985 
nnet RMSE 399 : 0.1499126 


s: 400 
logit 400 : -0.4815427 
logit mean 400 : -0.4407481 
logit RMSE 400 : 0.07152487 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 400 : -0.5543016 
boosting mean 400 : -0.4814771 
boosting RMSE 400 : 0.1400635 

forest 400 : -0.4639095 
forest mean 400 : -0.3905848 
forest RMSE 400 : 0.05099854 

nnet 400 : -0.05251045 
nnet mean 400 : -0.4156883 
nnet RMSE 400 : 0.1507298 


s: 401 
logit 401 : -0.432083 
logit mean 401 : -0.4407265 
logit RMSE 401 : 0.0714536 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 401 : -0.6696621 
boosting mean 401 : -0.4819464 
boosting RMSE 401 : 0.1405354 

forest 401 : -0.3100910 
forest mean 401 : -0.390384 
forest RMSE 401 : 0.05113241 

nnet 401 : -0.3211656 
nnet mean 401 : -0.4154526 
nnet RMSE 401 : 0.1505932 


s: 402 
logit 402 : -0.4244533 
logit mean 402 : -0.440686 
logit RMSE 402 : 0.07137509 

boosting 402 : -0.4942099 
boosting mean 402 : -0.4819769 
boosting RMSE 402 : 0.1404391 

forest 402 : -0.4524565 
forest mean 402 : -0.3905384 
forest RMSE 402 : 0.05113575 

nnet 402 : -0.5040407 
nnet mean 402 : -0.4156729 
nnet RMSE 402 : 0.1504953 


s: 403 
logit 403 : -0.418442 
logit mean 403 : -0.4406308 
logit RMSE 403 : 0.0712924 

boosting 403 : -0.4824685 
boosting mean 403 : -0.4819781 
boosting RMSE 403 : 0.1403249 

forest 403 : -0.3772316 
forest mean 403 : -0.3905054 
forest RMSE 403 : 0.05108486 

nnet 403 : -0.6425012 
nnet mean 403 : -0.4162358 
nnet RMSE 403 : 0.1507931 


s: 404 
logit 404 : -0.4785504 
logit mean 404 : -0.4407247 
logit RMSE 404 : 0.07131127 

boosting 404 : -0.5748458 
boosting mean 404 : -0.482208 
boosting RMSE 404 : 0.1404208 

forest 404 : -0.4037900 
forest mean 404 : -0.3905383 
forest RMSE 404 : 0.05102194 

nnet 404 : -0.1781459 
nnet mean 404 : -0.4156464 
nnet RMSE 404 : 0.1510103 


s: 405 
logit 405 : -0.4933523 
logit mean 405 : -0.4408546 
logit RMSE 405 : 0.07137408 

boosting 405 : -0.4572689 
boosting mean 405 : -0.4821464 
boosting RMSE 405 : 0.1402762 

forest 405 : -0.4347765 
forest mean 405 : -0.3906475 
forest RMSE 405 : 0.0509882 

nnet 405 : -0.302624 
nnet mean 405 : -0.4153674 
nnet RMSE 405 : 0.1509013 


s: 406 
logit 406 : -0.4148083 
logit mean 406 : -0.4407905 
logit RMSE 406 : 0.07128991 

boosting 406 : -0.4370712 
boosting mean 406 : -0.4820354 
boosting RMSE 406 : 0.1401154 

forest 406 : -0.3748278 
forest mean 406 : -0.3906086 
forest RMSE 406 : 0.05094069 

nnet 406 : -0.1982588 
nnet mean 406 : -0.4148326 
nnet RMSE 406 : 0.1510476 


s: 407 
logit 407 : -0.4410718 
logit mean 407 : -0.4407911 
logit RMSE 407 : 0.07123138 

boosting 407 : -0.5888995 
boosting mean 407 : -0.4822979 
boosting RMSE 407 : 0.1402561 

forest 407 : -0.4414444 
forest mean 407 : -0.3907335 
forest RMSE 407 : 0.05091953 

nnet 407 : -0.6891748 
nnet mean 407 : -0.4155067 
nnet RMSE 407 : 0.1515413 


s: 408 
logit 408 : -0.4527636 
logit mean 408 : -0.4408205 
logit RMSE 408 : 0.07119197 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 408 : -0.5878832 
boosting mean 408 : -0.4825567 
boosting RMSE 408 : 0.1403926 

forest 408 : -0.5117874 
forest mean 408 : -0.3910302 
forest RMSE 408 : 0.05115733 

nnet 408 : -0.6413189 
nnet mean 408 : -0.4160601 
nnet RMSE 408 : 0.1518263 


s: 409 
logit 409 : -0.4146797 
logit mean 409 : -0.4407566 
logit RMSE 409 : 0.07110859 

boosting 409 : -0.5084821 
boosting mean 409 : -0.4826201 
boosting RMSE 409 : 0.1403234 

forest 409 : -0.3746114 
forest mean 409 : -0.39099 
forest RMSE 409 : 0.05111017 

nnet 409 : -0.2452607 
nnet mean 409 : -0.4156425 
nnet RMSE 409 : 0.1518334 


s: 410 
logit 410 : -0.4749366 
logit mean 410 : -0.4408399 
logit RMSE 410 : 0.07111818 

boosting 410 : -0.4045546 
boosting mean 410 : -0.4824297 
boosting RMSE 410 : 0.1401524 

forest 410 : -0.4437442 
forest mean 410 : -0.3911187 
forest RMSE 410 : 0.0510935 

nnet 410 : -0.2027553 
nnet mean 410 : -0.4151233 
nnet RMSE 410 : 0.1519607 


s: 411 
logit 411 : -0.4967886 
logit mean 411 : -0.4409761 
logit RMSE 411 : 0.07119187 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 411 : -0.4480801 
boosting mean 411 : -0.4823461 
boosting RMSE 411 : 0.1400019 

forest 411 : -0.4091896 
forest mean 411 : -0.3911627 
forest RMSE 411 : 0.05103331 

nnet 411 : -0.3049988 
nnet mean 411 : -0.4148554 
nnet RMSE 411 : 0.1518481 


s: 412 
logit 412 : -0.4125004 
logit mean 412 : -0.440907 
logit RMSE 412 : 0.07110809 

boosting 412 : -0.5146911 
boosting mean 412 : -0.4824246 
boosting RMSE 412 : 0.1399460 

forest 412 : -0.3982226 
forest mean 412 : -0.3911798 
forest RMSE 412 : 0.05097142 

nnet 412 : -0.6711745 
nnet mean 412 : -0.4154775 
nnet RMSE 412 : 0.1522510 


s: 413 
logit 413 : -0.4009841 
logit mean 413 : -0.4408103 
logit RMSE 413 : 0.07102197 

boosting 413 : -0.4377643 
boosting mean 413 : -0.4823165 
boosting RMSE 413 : 0.1397888 

forest 413 : -0.5290653 
forest mean 413 : -0.3915137 
forest RMSE 413 : 0.05130427 

nnet 413 : -0.3358817 
nnet mean 413 : -0.4152848 
nnet RMSE 413 : 0.1520992 


s: 414 
logit 414 : -0.427817 
logit mean 414 : -0.4407789 
logit RMSE 414 : 0.07094931 

boosting 414 : -0.4215563 
boosting mean 414 : -0.4821697 
boosting RMSE 414 : 0.1396239 

forest 414 : -0.3935957 
forest mean 414 : -0.3915187 
forest RMSE 414 : 0.05124324 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 414 : -0.2881848 
nnet mean 414 : -0.4149778 
nnet RMSE 414 : 0.1520148 


s: 415 
logit 415 : -0.4279519 
logit mean 415 : -0.440748 
logit RMSE 415 : 0.07087706 

boosting 415 : -0.7098814 
boosting mean 415 : -0.4827184 
boosting RMSE 415 : 0.1402827 

forest 415 : -0.4104679 
forest mean 415 : -0.3915644 
forest RMSE 415 : 0.05118404 

nnet 415 : -0.4664275 
nnet mean 415 : -0.4151017 
nnet RMSE 415 : 0.1518666 


s: 416 
logit 416 : -0.4356648 
logit mean 416 : -0.4407358 
logit RMSE 416 : 0.07081341 

boosting 416 : -0.5442439 
boosting mean 416 : -0.4828663 
boosting RMSE 416 : 0.1402924 

forest 416 : -0.3739616 
forest mean 416 : -0.3915220 
forest RMSE 416 : 0.05113843 

nnet 416 : -0.4658650 
nnet mean 416 : -0.4152238 
nnet RMSE 416 : 0.1517183 


s: 417 
logit 417 : -0.415325 
logit mean 417 : -0.4406748 
logit RMSE 417 : 0.07073244 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 417 : -0.4666344 
boosting mean 417 : -0.4828274 
boosting RMSE 417 : 0.1401620 

forest 417 : -0.3845332 
forest mean 417 : -0.3915053 
forest RMSE 417 : 0.05108269 

nnet 417 : -0.2583216 
nnet mean 417 : -0.4148475 
nnet RMSE 417 : 0.151695 


s: 418 
logit 418 : -0.4967699 
logit mean 418 : -0.440809 
logit RMSE 418 : 0.07080615 

boosting 418 : -0.5222088 
boosting mean 418 : -0.4829216 
boosting RMSE 418 : 0.1401218 

forest 418 : -0.4163278 
forest mean 418 : -0.3915647 
forest RMSE 418 : 0.0510278 

nnet 418 : -0.7392822 
nnet mean 418 : -0.4156237 
nnet RMSE 418 : 0.1524195 


s: 419 
logit 419 : -0.4799781 
logit mean 419 : -0.4409025 
logit RMSE 419 : 0.07082946 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 419 : -0.5551355 
boosting mean 419 : -0.483094 
boosting RMSE 419 : 0.1401596 

forest 419 : -0.4032141 
forest mean 419 : -0.3915925 
forest RMSE 419 : 0.05096711 

nnet 419 : -0.5087485 
nnet mean 419 : -0.4158459 
nnet RMSE 419 : 0.1523302 


s: 420 
logit 420 : -0.4552213 
logit mean 420 : -0.4409366 
logit RMSE 420 : 0.07079638 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 420 : -0.6077882 
boosting mean 420 : -0.4833908 
boosting RMSE 420 : 0.1403593 

forest 420 : -0.4210448 
forest mean 420 : -0.3916626 
forest RMSE 420 : 0.05091675 

nnet 420 : -0.4483345 
nnet mean 420 : -0.4159233 
nnet RMSE 420 : 0.1521670 


s: 421 
logit 421 : -0.2748349 
logit mean 421 : -0.4405421 
logit RMSE 421 : 0.07097489 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 421 : -0.3861369 
boosting mean 421 : -0.4831598 
boosting RMSE 421 : 0.1401941 

forest 421 : -0.3001001 
forest mean 421 : -0.3914451 
forest RMSE 421 : 0.05108878 

nnet 421 : -0.3933813 
nnet mean 421 : -0.4158697 
nnet RMSE 421 : 0.1519865 


s: 422 
logit 422 : -0.4426960 
logit mean 422 : -0.4405472 
logit RMSE 422 : 0.0709212 

boosting 422 : -0.4772605 
boosting mean 422 : -0.4831459 
boosting RMSE 422 : 0.1400784 

forest 422 : -0.4008 
forest mean 422 : -0.3914673 
forest RMSE 422 : 0.05102823 

nnet 422 : -0.3609965 
nnet mean 422 : -0.4157397 
nnet RMSE 422 : 0.1518182 


s: 423 
logit 423 : -0.4075902 
logit mean 423 : -0.4404693 
logit RMSE 423 : 0.07083828 

boosting 423 : -0.4586851 
boosting mean 423 : -0.483088 
boosting RMSE 423 : 0.1399419 

forest 423 : -0.4310335 
forest mean 423 : -0.3915608 
forest RMSE 423 : 0.0509902 

nnet 423 : -0.7142497 
nnet mean 423 : -0.4164454 
nnet RMSE 423 : 0.1524065 


s: 424 
logit 424 : -0.4622318 
logit mean 424 : -0.4405206 
logit RMSE 424 : 0.07081922 

boosting 424 : -0.5555525 
boosting mean 424 : -0.4832589 
boosting RMSE 424 : 0.1399807 

forest 424 : -0.4147691 
forest mean 424 : -0.3916155 
forest RMSE 424 : 0.05093509 

nnet 424 : -0.2487272 
nnet mean 424 : -0.4160498 
nnet RMSE 424 : 0.1524038 


s: 425 
logit 425 : -0.4571446 
logit mean 425 : -0.4405597 
logit RMSE 425 : 0.07079014 

boosting 425 : -0.4519479 
boosting mean 425 : -0.4831853 
boosting RMSE 425 : 0.1398386 

forest 425 : -0.3193791 
forest mean 425 : -0.3914456 
forest RMSE 425 : 0.05102521 

nnet 425 : -0.4317318 
nnet mean 425 : -0.4160867 
nnet RMSE 425 : 0.1522322 


s: 426 
logit 426 : -0.4473108 
logit mean 426 : -0.4405756 
logit RMSE 426 : 0.07074415 

boosting 426 : -0.5510257 
boosting mean 426 : -0.4833445 
boosting RMSE 426 : 0.1398660 

forest 426 : -0.3582384 
forest mean 426 : -0.3913676 
forest RMSE 426 : 0.05100544 

nnet 426 : -0.5861196 
nnet mean 426 : -0.4164859 
nnet RMSE 426 : 0.1523206 


s: 427 
logit 427 : -0.4929145 
logit mean 427 : -0.4406981 
logit RMSE 427 : 0.07080418 

boosting 427 : -0.2309793 
boosting mean 427 : -0.4827535 
boosting RMSE 427 : 0.1399413 

forest 427 : -0.3599334 
forest mean 427 : -0.391294 
forest RMSE 427 : 0.05098256 

nnet 427 : -0.1831806 
nnet mean 427 : -0.4159395 
nnet RMSE 427 : 0.1525035 


s: 428 
logit 428 : -0.3885608 
logit mean 428 : -0.4405763 
logit RMSE 428 : 0.07072358 

boosting 428 : -0.4389905 
boosting mean 428 : -0.4826512 
boosting RMSE 428 : 0.1397905 

forest 428 : -0.344439 
forest mean 428 : -0.3911845 
forest RMSE 428 : 0.05099374 

nnet 428 : -0.4979964 
nnet mean 428 : -0.4161312 
nnet RMSE 428 : 0.1523989 


s: 429 
logit 429 : -0.5194624 
logit mean 429 : -0.4407602 
logit RMSE 429 : 0.07087617 

boosting 429 : -0.4442229 
boosting mean 429 : -0.4825617 
boosting RMSE 429 : 0.1396438 

forest 429 : -0.3763342 
forest mean 429 : -0.3911499 
forest RMSE 429 : 0.05094708 

nnet 429 : -0.3343704 
nnet mean 429 : -0.4159406 
nnet RMSE 429 : 0.1522541 


s: 430 
logit 430 : -0.3522781 
logit mean 430 : -0.4405544 
logit RMSE 430 : 0.07083111 

boosting 430 : -0.4340805 
boosting mean 430 : -0.4824489 
boosting RMSE 430 : 0.1394910 

forest 430 : -0.3943063 
forest mean 430 : -0.3911573 
forest RMSE 430 : 0.05088855 

nnet 430 : -0.4999966 
nnet mean 430 : -0.4161361 
nnet RMSE 430 : 0.1521534 


s: 431 
logit 431 : -0.4838941 
logit mean 431 : -0.440655 
logit RMSE 431 : 0.0708642 

boosting 431 : -0.4660358 
boosting mean 431 : -0.4824108 
boosting RMSE 431 : 0.1393654 

forest 431 : -0.3401719 
forest mean 431 : -0.391039 
forest RMSE 431 : 0.05091111 

nnet 431 : -0.5046593 
nnet mean 431 : -0.4163415 
nnet RMSE 431 : 0.1520604 


s: 432 
logit 432 : -0.5375971 
logit mean 432 : -0.4408794 
logit RMSE 432 : 0.07109105 

boosting 432 : -0.4816794 
boosting mean 432 : -0.4824092 
boosting RMSE 432 : 0.1392594 

forest 432 : -0.422965 
forest mean 432 : -0.3911129 
forest RMSE 432 : 0.05086415 

nnet 432 : -0.2803537 
nnet mean 432 : -0.4160267 
nnet RMSE 432 : 0.1519934 


s: 433 
logit 433 : -0.4121441 
logit mean 433 : -0.440813 
logit RMSE 433 : 0.07101131 

boosting 433 : -0.7167876 
boosting mean 433 : -0.4829504 
boosting RMSE 433 : 0.1399291 

forest 433 : -0.3667585 
forest mean 433 : -0.3910566 
forest RMSE 433 : 0.05083049 

nnet 433 : -0.2508141 
nnet mean 433 : -0.4156452 
nnet RMSE 433 : 0.1519869 


s: 434 
logit 434 : -0.5233584 
logit mean 434 : -0.4410032 
logit RMSE 434 : 0.07117619 

boosting 434 : -0.4409808 
boosting mean 434 : -0.4828537 
boosting RMSE 434 : 0.1397817 

forest 434 : -0.4353665 
forest mean 434 : -0.3911587 
forest RMSE 434 : 0.05080027 

nnet 434 : -0.4400265 
nnet mean 434 : -0.4157013 
nnet RMSE 434 : 0.1518239 


s: 435 
logit 435 : -0.4808925 
logit mean 435 : -0.4410949 
logit RMSE 435 : 0.07120005 

boosting 435 : -0.4326779 
boosting mean 435 : -0.4827384 
boosting RMSE 435 : 0.1396297 

forest 435 : -0.4084698 
forest mean 435 : -0.3911985 
forest RMSE 435 : 0.05074347 

nnet 435 : -0.5676817 
nnet mean 435 : -0.4160507 
nnet RMSE 435 : 0.1518622 


s: 436 
logit 436 : -0.4046048 
logit mean 436 : -0.4410112 
logit RMSE 436 : 0.07111869 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 436 : -0.5204255 
boosting mean 436 : -0.4828248 
boosting RMSE 436 : 0.1395887 

forest 436 : -0.4540188 
forest mean 436 : -0.3913426 
forest RMSE 436 : 0.05075123 

nnet 436 : -0.6470494 
nnet mean 436 : -0.4165805 
nnet RMSE 436 : 0.1521487 


s: 437 
logit 437 : -0.4234515 
logit mean 437 : -0.440971 
logit RMSE 437 : 0.07104613 

boosting 437 : -0.5421701 
boosting mean 437 : -0.4829606 
boosting RMSE 437 : 0.1395946 

forest 437 : -0.3786172 
forest mean 437 : -0.3913135 
forest RMSE 437 : 0.05070344 

nnet 437 : -0.5823602 
nnet mean 437 : -0.4169599 
nnet RMSE 437 : 0.1522247 


s: 438 
logit 438 : -0.3545982 
logit mean 438 : -0.4407738 
logit RMSE 438 : 0.07099813 

boosting 438 : -0.4316013 
boosting mean 438 : -0.4828434 
boosting RMSE 438 : 0.1394434 

forest 438 : -0.3552812 
forest mean 438 : -0.3912312 
forest RMSE 438 : 0.05069058 

nnet 438 : -0.4020074 
nnet mean 438 : -0.4169257 
nnet RMSE 438 : 0.1520509 


s: 439 
logit 439 : -0.4852173 
logit mean 439 : -0.4408751 
logit RMSE 439 : 0.07103375 

boosting 439 : -0.5290582 
boosting mean 439 : -0.4829486 
boosting RMSE 439 : 0.1394206 

forest 439 : -0.4263451 
forest mean 439 : -0.3913112 
forest RMSE 439 : 0.05064843 

nnet 439 : -0.4391771 
nnet mean 439 : -0.4169764 
nnet RMSE 439 : 0.1518891 


s: 440 
logit 440 : -0.404156 
logit mean 440 : -0.4407916 
logit RMSE 440 : 0.07095327 

boosting 440 : -0.4471272 
boosting mean 440 : -0.4828672 
boosting RMSE 440 : 0.1392802 

forest 440 : -0.3430519 
forest mean 440 : -0.3912015 
forest RMSE 440 : 0.05066363 

nnet 440 : -0.3805249 
nnet mean 440 : -0.4168936 
nnet RMSE 440 : 0.1517192 


s: 441 
logit 441 : -0.3943257 
logit mean 441 : -0.4406863 
logit RMSE 441 : 0.07087329 

boosting 441 : -0.4995446 
boosting mean 441 : -0.4829050 
boosting RMSE 441 : 0.1392029 

forest 441 : -0.392457 
forest mean 441 : -0.3912044 
forest RMSE 441 : 0.05060743 

nnet 441 : -0.7265457 
nnet mean 441 : -0.4175957 
nnet RMSE 441 : 0.1523428 


s: 442 
logit 442 : -0.4155530 
logit mean 442 : -0.4406294 
logit RMSE 442 : 0.07079694 

boosting 442 : -0.4757156 
boosting mean 442 : -0.4828888 
boosting RMSE 442 : 0.139092 

forest 442 : -0.3245201 
forest mean 442 : -0.3910535 
forest RMSE 442 : 0.05067749 

nnet 442 : -0.2417485 
nnet mean 442 : -0.4171979 
nnet RMSE 442 : 0.1523564 


s: 443 
logit 443 : -0.4066305 
logit mean 443 : -0.4405527 
logit RMSE 443 : 0.07071769 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 443 : -0.3700211 
boosting mean 443 : -0.482634 
boosting RMSE 443 : 0.1389422 

forest 443 : -0.3397586 
forest mean 443 : -0.3909377 
forest RMSE 443 : 0.05070111 

nnet 443 : -0.4561126 
nnet mean 443 : -0.4172857 
nnet RMSE 443 : 0.1522077 


s: 444 
logit 444 : -0.4851114 
logit mean 444 : -0.440653 
logit RMSE 444 : 0.0707534 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 444 : -0.4299596 
boosting mean 444 : -0.4825154 
boosting RMSE 444 : 0.1387929 

forest 444 : -0.4462418 
forest mean 444 : -0.3910623 
forest RMSE 444 : 0.0506915 

nnet 444 : -0.4682031 
nnet mean 444 : -0.4174004 
nnet RMSE 444 : 0.1520706 


s: 445 
logit 445 : -0.4319628 
logit mean 445 : -0.4406335 
logit RMSE 445 : 0.07069009 

boosting 445 : -0.5521527 
boosting mean 445 : -0.4826719 
boosting RMSE 445 : 0.1388244 

forest 445 : -0.3654849 
forest mean 445 : -0.3910048 
forest RMSE 445 : 0.05066094 

nnet 445 : -0.4512876 
nnet mean 445 : -0.4174766 
nnet RMSE 445 : 0.1519191 


s: 446 
logit 446 : -0.4448478 
logit mean 446 : -0.4406429 
logit RMSE 446 : 0.07064272 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 446 : -0.3795539 
boosting mean 446 : -0.4824406 
boosting RMSE 446 : 0.1386721 

forest 446 : -0.3549681 
forest mean 446 : -0.390924 
forest RMSE 446 : 0.05064902 

nnet 446 : -0.3728297 
nnet mean 446 : -0.4173765 
nnet RMSE 446 : 0.1517542 


s: 447 
logit 447 : -0.4264991 
logit mean 447 : -0.4406113 
logit RMSE 447 : 0.07057479 

boosting 447 : -0.4765804 
boosting mean 447 : -0.4824275 
boosting RMSE 447 : 0.1385642 

forest 447 : -0.3258947 
forest mean 447 : -0.3907785 
forest RMSE 447 : 0.05071361 

nnet 447 : -0.3584386 
nnet mean 447 : -0.4172446 
nnet RMSE 447 : 0.1515971 


s: 448 
logit 448 : -0.5096243 
logit mean 448 : -0.4407653 
logit RMSE 448 : 0.07068598 

boosting 448 : -0.4449384 
boosting mean 448 : -0.4823439 
boosting RMSE 448 : 0.1384258 

forest 448 : -0.4301025 
forest mean 448 : -0.3908663 
forest RMSE 448 : 0.05067693 

nnet 448 : -0.3534412 
nnet mean 448 : -0.4171022 
nnet RMSE 448 : 0.1514438 


s: 449 
logit 449 : -0.3386099 
logit mean 449 : -0.4405378 
logit RMSE 449 : 0.07066664 

boosting 449 : -0.6904772 
boosting mean 449 : -0.4828074 
boosting RMSE 449 : 0.1389494 

forest 449 : -0.4498113 
forest mean 449 : -0.3909976 
forest RMSE 449 : 0.05067502 

nnet 449 : -0.3273012 
nnet mean 449 : -0.4169022 
nnet RMSE 449 : 0.1513139 


s: 450 
logit 450 : -0.3345946 
logit mean 450 : -0.4403024 
logit RMSE 450 : 0.07065538 

boosting 450 : -0.6608949 
boosting mean 450 : -0.4832032 
boosting RMSE 450 : 0.1393388 

forest 450 : -0.4473596 
forest mean 450 : -0.3911228 
forest RMSE 450 : 0.0506679 

nnet 450 : -0.411958 
nnet mean 450 : -0.4168912 
nnet RMSE 450 : 0.1511468 


s: 451 
logit 451 : -0.5033258 
logit mean 451 : -0.4404421 
logit RMSE 451 : 0.07074451 

boosting 451 : -0.5014161 
boosting mean 451 : -0.4832435 
boosting RMSE 451 : 0.1392661 

forest 451 : -0.362452 
forest mean 451 : -0.3910592 
forest RMSE 451 : 0.05064257 

nnet 451 : -0.4743716 
nnet mean 451 : -0.4170187 
nnet RMSE 451 : 0.1510197 


s: 452 
logit 452 : -0.4241029 
logit mean 452 : -0.440406 
logit RMSE 452 : 0.0706753 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 452 : -0.6820956 
boosting mean 452 : -0.4836835 
boosting RMSE 452 : 0.1397433 

forest 452 : -0.4321357 
forest mean 452 : -0.3911501 
forest RMSE 452 : 0.05060909 

nnet 452 : -0.6183737 
nnet mean 452 : -0.4174641 
nnet RMSE 452 : 0.1512019 


s: 453 
logit 453 : -0.4136380 
logit mean 453 : -0.4403469 
logit RMSE 453 : 0.07060016 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 453 : -0.4262919 
boosting mean 453 : -0.4835568 
boosting RMSE 453 : 0.1395945 

forest 453 : -0.2800594 
forest mean 453 : -0.3909049 
forest RMSE 453 : 0.05086632 

nnet 453 : -0.2669680 
nnet mean 453 : -0.4171319 
nnet RMSE 453 : 0.1511641 


s: 454 
logit 454 : -0.2598848 
logit mean 454 : -0.4399494 
logit RMSE 454 : 0.07082829 

boosting 454 : -0.2571232 
boosting mean 454 : -0.483058 
boosting RMSE 454 : 0.1396018 

forest 454 : -0.2905233 
forest mean 454 : -0.3906838 
forest RMSE 454 : 0.05106939 

nnet 454 : -0.6762282 
nnet mean 454 : -0.4177026 
nnet RMSE 454 : 0.1515531 


s: 455 
logit 455 : -0.510553 
logit mean 455 : -0.4401046 
logit RMSE 455 : 0.07094 

boosting 455 : -0.3733780 
boosting mean 455 : -0.482817 
boosting RMSE 455 : 0.1394539 

forest 455 : -0.4146102 
forest mean 455 : -0.3907364 
forest RMSE 455 : 0.05101784 

nnet 455 : -0.4674682 
nnet mean 455 : -0.417812 
nnet RMSE 455 : 0.1514195 


s: 456 
logit 456 : -0.4381774 
logit mean 456 : -0.4401003 
logit RMSE 456 : 0.07088472 

boosting 456 : -0.4287102 
boosting mean 456 : -0.4826983 
boosting RMSE 456 : 0.1393074 

forest 456 : -0.4381685 
forest mean 456 : -0.3908404 
forest RMSE 456 : 0.0509932 

nnet 456 : -0.467154 
nnet mean 456 : -0.4179202 
nnet RMSE 456 : 0.1512860 


s: 457 
logit 457 : -0.4073221 
logit mean 457 : -0.4400286 
logit RMSE 457 : 0.07080795 

boosting 457 : -0.4835901 
boosting mean 457 : -0.4827003 
boosting RMSE 457 : 0.1392098 

forest 457 : -0.3874176 
forest mean 457 : -0.3908329 
forest RMSE 457 : 0.05094078 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 457 : -0.3158444 
nnet mean 457 : -0.4176968 
nnet RMSE 457 : 0.1511717 


s: 458 
logit 458 : -0.3147303 
logit mean 458 : -0.439755 
logit RMSE 458 : 0.07084274 

boosting 458 : -0.3986378 
boosting mean 458 : -0.4825167 
boosting RMSE 458 : 0.1390577 

forest 458 : -0.3939495 
forest mean 458 : -0.3908397 
forest RMSE 458 : 0.05088592 

nnet 458 : -0.4375409 
nnet mean 458 : -0.4177402 
nnet RMSE 458 : 0.1510168 


s: 459 
logit 459 : -0.3579373 
logit mean 459 : -0.4395768 
logit RMSE 459 : 0.07079276 

boosting 459 : -0.805823 
boosting mean 459 : -0.4832211 
boosting RMSE 459 : 0.1401918 

forest 459 : -0.4536966 
forest mean 459 : -0.3909766 
forest RMSE 459 : 0.05089222 

nnet 459 : -0.5035586 
nnet mean 459 : -0.4179271 
nnet RMSE 459 : 0.1509296 


s: 460 
logit 460 : -0.4038794 
logit mean 460 : -0.4394992 
logit RMSE 460 : 0.070716 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 460 : -0.4882289 
boosting mean 460 : -0.483232 
boosting RMSE 460 : 0.1400997 

forest 460 : -0.3666643 
forest mean 460 : -0.3909238 
forest RMSE 460 : 0.05086062 

nnet 460 : -0.4295517 
nnet mean 460 : -0.4179524 
nnet RMSE 460 : 0.1507717 


s: 461 
logit 461 : -0.4119452 
logit mean 461 : -0.4394394 
logit RMSE 461 : 0.07064145 

boosting 461 : -0.6057753 
boosting mean 461 : -0.4834978 
boosting RMSE 461 : 0.1402755 

forest 461 : -0.4830396 
forest mean 461 : -0.3911236 
forest RMSE 461 : 0.05095242 

nnet 461 : -0.7304259 
nnet mean 461 : -0.4186302 
nnet RMSE 461 : 0.1513923 


s: 462 
logit 462 : -0.4372248 
logit mean 462 : -0.4394346 
logit RMSE 462 : 0.0705862 

boosting 462 : -0.6273609 
boosting mean 462 : -0.4838092 
boosting RMSE 462 : 0.1405223 

forest 462 : -0.384535 
forest mean 462 : -0.3911093 
forest RMSE 462 : 0.05090234 

nnet 462 : -0.4959922 
nnet mean 462 : -0.4187977 
nnet RMSE 462 : 0.1512943 


s: 463 
logit 463 : -0.3961583 
logit mean 463 : -0.4393412 
logit RMSE 463 : 0.07051016 

boosting 463 : -0.5460114 
boosting mean 463 : -0.4839435 
boosting RMSE 463 : 0.1405343 

forest 463 : -0.3367892 
forest mean 463 : -0.390992 
forest RMSE 463 : 0.05093213 

nnet 463 : -0.4319473 
nnet mean 463 : -0.4188261 
nnet RMSE 463 : 0.1511382 


s: 464 
logit 464 : -0.4567267 
logit mean 464 : -0.4393786 
logit RMSE 464 : 0.07048335 

boosting 464 : -0.4423012 
boosting mean 464 : -0.4838538 
boosting RMSE 464 : 0.1403966 

forest 464 : -0.398605 
forest mean 464 : -0.3910084 
forest RMSE 464 : 0.05087725 

nnet 464 : -0.680522 
nnet mean 464 : -0.4193901 
nnet RMSE 464 : 0.1515358 


s: 465 
logit 465 : -0.4520015 
logit mean 465 : -0.4394058 
logit RMSE 465 : 0.07044881 

boosting 465 : -0.4944038 
boosting mean 465 : -0.4838765 
boosting RMSE 465 : 0.1403138 

forest 465 : -0.451392 
forest mean 465 : -0.3911383 
forest RMSE 465 : 0.05087837 

nnet 465 : -0.4447017 
nnet mean 465 : -0.4194445 
nnet RMSE 465 : 0.151387 


s: 466 
logit 466 : -0.5440486 
logit mean 466 : -0.4396303 
logit RMSE 466 : 0.07068884 

boosting 466 : -0.4496369 
boosting mean 466 : -0.483803 
boosting RMSE 466 : 0.1401821 

forest 466 : -0.391753 
forest mean 466 : -0.3911396 
forest RMSE 466 : 0.05082518 

nnet 466 : -0.5890935 
nnet mean 466 : -0.4198086 
nnet RMSE 466 : 0.1514780 


s: 467 
logit 467 : -0.4518097 
logit mean 467 : -0.4396564 
logit RMSE 467 : 0.0706538 

boosting 467 : -0.2874117 
boosting mean 467 : -0.4833825 
boosting RMSE 467 : 0.1401288 

forest 467 : -0.4355888 
forest mean 467 : -0.3912348 
forest RMSE 467 : 0.05079744 

nnet 467 : -0.3210912 
nnet mean 467 : -0.4195972 
nnet RMSE 467 : 0.1513598 


s: 468 
logit 468 : -0.4057871 
logit mean 468 : -0.439584 
logit RMSE 468 : 0.07057879 

boosting 468 : -0.5491726 
boosting mean 468 : -0.4835231 
boosting RMSE 468 : 0.1401487 

forest 468 : -0.3963109 
forest mean 468 : -0.3912456 
forest RMSE 468 : 0.05074342 

nnet 468 : -0.5302421 
nnet mean 468 : -0.4198336 
nnet RMSE 468 : 0.1513178 


s: 469 
logit 469 : -0.4713664 
logit mean 469 : -0.4396518 
logit RMSE 469 : 0.07058048 

boosting 469 : -0.310981 
boosting mean 469 : -0.4831552 
boosting RMSE 469 : 0.1400596 

forest 469 : -0.3496879 
forest mean 469 : -0.391157 
forest RMSE 469 : 0.05074251 

nnet 469 : -0.4101031 
nnet mean 469 : -0.4198128 
nnet RMSE 469 : 0.1511571 


s: 470 
logit 470 : -0.4955652 
logit mean 470 : -0.4397708 
logit RMSE 470 : 0.07064302 

boosting 470 : -0.5953935 
boosting mean 470 : -0.483394 
boosting RMSE 470 : 0.1402005 

forest 470 : -0.3883026 
forest mean 470 : -0.3911510 
forest RMSE 470 : 0.05069137 

nnet 470 : -0.4196326 
nnet mean 470 : -0.4198125 
nnet RMSE 470 : 0.1509989 


s: 471 
logit 471 : -0.47535 
logit mean 471 : -0.4398463 
logit RMSE 471 : 0.07065334 

boosting 471 : -0.4519574 
boosting mean 471 : -0.4833272 
boosting RMSE 471 : 0.1400720 

forest 471 : -0.3520735 
forest mean 471 : -0.391068 
forest RMSE 471 : 0.05068566 

nnet 471 : -0.3321888 
nnet mean 471 : -0.4196264 
nnet RMSE 471 : 0.1508709 


s: 472 
logit 472 : -0.3505456 
logit mean 472 : -0.4396571 
logit RMSE 472 : 0.07061516 

boosting 472 : -0.5487536 
boosting mean 472 : -0.4834658 
boosting RMSE 472 : 0.1400910 

forest 472 : -0.3528745 
forest mean 472 : -0.3909871 
forest RMSE 472 : 0.05067838 

nnet 472 : -0.3632302 
nnet mean 472 : -0.4195069 
nnet RMSE 472 : 0.1507205 


s: 473 
logit 473 : -0.4624159 
logit mean 473 : -0.4397052 
logit RMSE 473 : 0.07059883 

boosting 473 : -0.407029 
boosting mean 473 : -0.4833042 
boosting RMSE 473 : 0.1399432 

forest 473 : -0.3668476 
forest mean 473 : -0.390936 
forest RMSE 473 : 0.05064773 

nnet 473 : -0.3064614 
nnet mean 473 : -0.4192679 
nnet RMSE 473 : 0.1506225 


s: 474 
logit 474 : -0.3854613 
logit mean 474 : -0.4395908 
logit RMSE 474 : 0.07052748 

boosting 474 : -0.4158783 
boosting mean 474 : -0.483162 
boosting RMSE 474 : 0.1397974 

forest 474 : -0.296028 
forest mean 474 : -0.3907358 
forest RMSE 474 : 0.05081916 

nnet 474 : -0.3190049 
nnet mean 474 : -0.4190564 
nnet RMSE 474 : 0.1505095 


s: 475 
logit 475 : -0.424132 
logit mean 475 : -0.4395582 
logit RMSE 475 : 0.0704619 

boosting 475 : -0.609407 
boosting mean 475 : -0.4834278 
boosting RMSE 475 : 0.1399803 

forest 475 : -0.4414097 
forest mean 475 : -0.3908425 
forest RMSE 475 : 0.05080118 

nnet 475 : -0.4292158 
nnet mean 475 : -0.4190778 
nnet RMSE 475 : 0.1503570 


s: 476 
logit 476 : -0.4112644 
logit mean 476 : -0.4394988 
logit RMSE 476 : 0.07038974 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 476 : -0.4400237 
boosting mean 476 : -0.4833366 
boosting RMSE 476 : 0.1398452 

forest 476 : -0.4078157 
forest mean 476 : -0.3908781 
forest RMSE 476 : 0.05074905 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 476 : -0.232832 
nnet mean 476 : -0.4186865 
nnet RMSE 476 : 0.1503943 


s: 477 
logit 477 : -0.3702899 
logit mean 477 : -0.4393537 
logit RMSE 477 : 0.07032907 

boosting 477 : -0.5225847 
boosting mean 477 : -0.4834189 
boosting RMSE 477 : 0.1398113 

forest 477 : -0.3232868 
forest mean 477 : -0.3907364 
forest RMSE 477 : 0.05081736 

nnet 477 : -0.4429569 
nnet mean 477 : -0.4187374 
nnet RMSE 477 : 0.1502494 


s: 478 
logit 478 : -0.3515428 
logit mean 478 : -0.43917 
logit RMSE 478 : 0.07029042 

boosting 478 : -0.6750913 
boosting mean 478 : -0.4838199 
boosting RMSE 478 : 0.1402306 

forest 478 : -0.3988008 
forest mean 478 : -0.3907533 
forest RMSE 478 : 0.05076421 

nnet 478 : -0.0009740763 
nnet mean 478 : -0.4178634 
nnet RMSE 478 : 0.1511977 


s: 479 
logit 479 : -0.4756557 
logit mean 479 : -0.4392462 
logit RMSE 479 : 0.07030205 

boosting 479 : -0.5345537 
boosting mean 479 : -0.4839258 
boosting RMSE 479 : 0.1402190 

forest 479 : -0.4225108 
forest mean 479 : -0.3908196 
forest RMSE 479 : 0.05072162 

nnet 479 : -0.4833796 
nnet mean 479 : -0.4180002 
nnet RMSE 479 : 0.1510879 


s: 480 
logit 480 : -0.4068882 
logit mean 480 : -0.4391788 
logit RMSE 480 : 0.07022948 

boosting 480 : -0.4557292 
boosting mean 480 : -0.483867 
boosting RMSE 480 : 0.1400959 

forest 480 : -0.3126106 
forest mean 480 : -0.3906567 
forest RMSE 480 : 0.05082552 

nnet 480 : -0.01601099 
nnet mean 480 : -0.4171627 
nnet RMSE 480 : 0.1519446 


s: 481 
logit 481 : -0.345084 
logit mean 481 : -0.4389831 
logit RMSE 481 : 0.07020111 

boosting 481 : -0.6272536 
boosting mean 481 : -0.4841651 
boosting RMSE 481 : 0.1403333 

forest 481 : -0.4009828 
forest mean 481 : -0.3906781 
forest RMSE 481 : 0.05077268 

nnet 481 : -0.6680648 
nnet mean 481 : -0.4176844 
nnet RMSE 481 : 0.1522779 


s: 482 
logit 482 : -0.4323713 
logit mean 482 : -0.4389694 
logit RMSE 482 : 0.07014375 

boosting 482 : -0.4114103 
boosting mean 482 : -0.4840142 
boosting RMSE 482 : 0.1401886 

forest 482 : -0.3081527 
forest mean 482 : -0.3905069 
forest RMSE 482 : 0.05089222 

nnet 482 : -0.2955169 
nnet mean 482 : -0.4174309 
nnet RMSE 482 : 0.1521943 


s: 483 
logit 483 : -0.5391151 
logit mean 483 : -0.4391768 
logit RMSE 483 : 0.07035643 

boosting 483 : -0.453856 
boosting mean 483 : -0.4839517 
boosting RMSE 483 : 0.1400648 

forest 483 : -0.4958033 
forest mean 483 : -0.3907249 
forest RMSE 483 : 0.05102606 

nnet 483 : -0.1557963 
nnet mean 483 : -0.4168892 
nnet RMSE 483 : 0.1524422 


s: 484 
logit 484 : -0.5306734 
logit mean 484 : -0.4393658 
logit RMSE 484 : 0.07053425 

boosting 484 : -0.7260222 
boosting mean 484 : -0.4844519 
boosting RMSE 484 : 0.1407026 

forest 484 : -0.3835365 
forest mean 484 : -0.3907101 
forest RMSE 484 : 0.05097881 

nnet 484 : -0.6190417 
nnet mean 484 : -0.4173069 
nnet RMSE 484 : 0.1526097 


s: 485 
logit 485 : -0.3473303 
logit mean 485 : -0.439176 
logit RMSE 485 : 0.07050207 

boosting 485 : -0.3766688 
boosting mean 485 : -0.4842297 
boosting RMSE 485 : 0.1405615 

forest 485 : -0.3091286 
forest mean 485 : -0.3905419 
forest RMSE 485 : 0.05109312 

nnet 485 : -0.3777649 
nnet mean 485 : -0.4172254 
nnet RMSE 485 : 0.1524557 


s: 486 
logit 486 : -0.5110052 
logit mean 486 : -0.4393238 
logit RMSE 486 : 0.07060927 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 486 : -0.4516832 
boosting mean 486 : -0.4841627 
boosting RMSE 486 : 0.1404364 

forest 486 : -0.5044641 
forest mean 486 : -0.3907763 
forest RMSE 486 : 0.05126002 

nnet 486 : -0.641335 
nnet mean 486 : -0.4176865 
nnet RMSE 486 : 0.1526917 


s: 487 
logit 487 : -0.4047437 
logit mean 487 : -0.4392528 
logit RMSE 487 : 0.07053706 

boosting 487 : -0.4359519 
boosting mean 487 : -0.4840637 
boosting RMSE 487 : 0.1403016 

forest 487 : -0.4001527 
forest mean 487 : -0.3907955 
forest RMSE 487 : 0.05120736 

nnet 487 : -0.604196 
nnet mean 487 : -0.4180695 
nnet RMSE 487 : 0.1528152 


s: 488 
logit 488 : -0.4649762 
logit mean 488 : -0.4393055 
logit RMSE 488 : 0.07052612 

boosting 488 : -0.3200589 
boosting mean 488 : -0.4837276 
boosting RMSE 488 : 0.1402045 

forest 488 : -0.4713074 
forest mean 488 : -0.3909605 
forest RMSE 488 : 0.05125661 

nnet 488 : -0.4731735 
nnet mean 488 : -0.4181824 
nnet RMSE 488 : 0.1526945 


s: 489 
logit 489 : -0.3523621 
logit mean 489 : -0.4391277 
logit RMSE 489 : 0.0704869 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 489 : -0.5091044 
boosting mean 489 : -0.4837795 
boosting RMSE 489 : 0.1401479 

forest 489 : -0.3257264 
forest mean 489 : -0.3908271 
forest RMSE 489 : 0.05131422 

nnet 489 : -0.2998633 
nnet mean 489 : -0.4179404 
nnet RMSE 489 : 0.1526055 


s: 490 
logit 490 : -0.4296523 
logit mean 490 : -0.4391084 
logit RMSE 490 : 0.07042767 

boosting 490 : -0.3642228 
boosting mean 490 : -0.4835355 
boosting RMSE 490 : 0.1400142 

forest 490 : -0.3984528 
forest mean 490 : -0.3908427 
forest RMSE 490 : 0.05126188 

nnet 490 : -0.5315472 
nnet mean 490 : -0.4181723 
nnet RMSE 490 : 0.1525655 


s: 491 
logit 491 : -0.4408261 
logit mean 491 : -0.4391119 
logit RMSE 491 : 0.07038004 

boosting 491 : -0.3881458 
boosting mean 491 : -0.4833412 
boosting RMSE 491 : 0.1398725 

forest 491 : -0.5465362 
forest mean 491 : -0.3911598 
forest RMSE 491 : 0.05163488 

nnet 491 : -0.5242762 
nnet mean 491 : -0.4183884 
nnet RMSE 491 : 0.1525132 


s: 492 
logit 492 : -0.4608622 
logit mean 492 : -0.4391561 
logit RMSE 492 : 0.070362 

boosting 492 : -0.5236931 
boosting mean 492 : -0.4834233 
boosting RMSE 492 : 0.1398415 

forest 492 : -0.4608833 
forest mean 492 : -0.3913015 
forest RMSE 492 : 0.05165536 

nnet 492 : -0.3566834 
nnet mean 492 : -0.4182629 
nnet RMSE 492 : 0.1523706 


s: 493 
logit 493 : -0.4284133 
logit mean 493 : -0.4391343 
logit RMSE 493 : 0.07030225 

boosting 493 : -0.3081494 
boosting mean 493 : -0.4830677 
boosting RMSE 493 : 0.1397609 

forest 493 : -0.4251662 
forest mean 493 : -0.3913702 
forest RMSE 493 : 0.05161539 

nnet 493 : -0.3604913 
nnet mean 493 : -0.4181458 
nnet RMSE 493 : 0.1522264 


s: 494 
logit 494 : -0.4719082 
logit mean 494 : -0.4392007 
logit RMSE 494 : 0.07030554 

boosting 494 : -0.4183715 
boosting mean 494 : -0.4829368 
boosting RMSE 494 : 0.1396218 

forest 494 : -0.5376805 
forest mean 494 : -0.3916664 
forest RMSE 494 : 0.05193388 

nnet 494 : -0.4090014 
nnet mean 494 : -0.4181273 
nnet RMSE 494 : 0.1520728 


s: 495 
logit 495 : -0.3892307 
logit mean 495 : -0.4390997 
logit RMSE 495 : 0.07023615 

boosting 495 : -0.67697 
boosting mean 495 : -0.4833288 
boosting RMSE 495 : 0.1400351 

forest 495 : -0.4518096 
forest mean 495 : -0.3917879 
forest RMSE 495 : 0.05193363 

nnet 495 : -0.2263334 
nnet mean 495 : -0.4177398 
nnet RMSE 495 : 0.1521195 


s: 496 
logit 496 : -0.4412987 
logit mean 496 : -0.4391042 
logit RMSE 496 : 0.07018982 

boosting 496 : -0.5403139 
boosting mean 496 : -0.4834436 
boosting RMSE 496 : 0.1400357 

forest 496 : -0.3972458 
forest mean 496 : -0.3917989 
forest RMSE 496 : 0.0518814 

nnet 496 : -0.3069632 
nnet mean 496 : -0.4175165 
nnet RMSE 496 : 0.1520235 


s: 497 
logit 497 : -0.510534 
logit mean 497 : -0.4392479 
logit RMSE 497 : 0.07029424 

boosting 497 : -0.5050593 
boosting mean 497 : -0.4834871 
boosting RMSE 497 : 0.1399741 

forest 497 : -0.4729688 
forest mean 497 : -0.3919622 
forest RMSE 497 : 0.05193242 

nnet 497 : -0.4448701 
nnet mean 497 : -0.4175715 
nnet RMSE 497 : 0.1518838 


s: 498 
logit 498 : -0.5299357 
logit mean 498 : -0.43943 
logit RMSE 498 : 0.0704646 

boosting 498 : -0.4767868 
boosting mean 498 : -0.4834737 
boosting RMSE 498 : 0.1398758 

forest 498 : -0.3722978 
forest mean 498 : -0.3919227 
forest RMSE 498 : 0.05189511 

nnet 498 : -0.7027516 
nnet mean 498 : -0.4181441 
nnet RMSE 498 : 0.1523366 


s: 499 
logit 499 : -0.5204686 
logit mean 499 : -0.4395924 
logit RMSE 499 : 0.07060024 

boosting 499 : -0.5345222 
boosting mean 499 : -0.483576 
boosting RMSE 499 : 0.1398653 

forest 499 : -0.4430328 
forest mean 499 : -0.3920251 
forest RMSE 499 : 0.05187886 

nnet 499 : -0.3842590 
nnet mean 499 : -0.4180762 
nnet RMSE 499 : 0.1521855 


s: 500 
logit 500 : -0.4134394 
logit mean 500 : -0.4395401 
logit RMSE 500 : 0.07053216 

boosting 500 : -0.5483001 
boosting mean 500 : -0.4837054 
boosting RMSE 500 : 0.1398827 

forest 500 : -0.4108787 
forest mean 500 : -0.3920628 
forest RMSE 500 : 0.05182924 

nnet 500 : -0.3593146 
nnet mean 500 : -0.4179587 
nnet RMSE 500 : 0.1520441 


s: 501 
logit 501 : -0.4712931 
logit mean 501 : -0.4396035 
logit RMSE 501 : 0.07053369 

boosting 501 : -0.5147442 
boosting mean 501 : -0.4837674 
boosting RMSE 501 : 0.139837 

forest 501 : -0.4031939 
forest mean 501 : -0.3920850 
forest RMSE 501 : 0.05177768 

nnet 501 : -0.5074581 
nnet mean 501 : -0.4181374 
nnet RMSE 501 : 0.1519681 


s: 502 
logit 502 : -0.4394001 
logit mean 502 : -0.4396030 
logit RMSE 502 : 0.07048534 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 502 : -0.4907682 
boosting mean 502 : -0.4837813 
boosting RMSE 502 : 0.1397564 

forest 502 : -0.5405372 
forest mean 502 : -0.3923808 
forest RMSE 502 : 0.05210501 

nnet 502 : -0.5784078 
nnet mean 502 : -0.4184566 
nnet RMSE 502 : 0.1520254 


s: 503 
logit 503 : -0.3345017 
logit mean 503 : -0.4393941 
logit RMSE 503 : 0.07047578 

boosting 503 : -0.4289741 
boosting mean 503 : -0.4836724 
boosting RMSE 503 : 0.1396234 

forest 503 : -0.3456054 
forest mean 503 : -0.3922878 
forest RMSE 503 : 0.05210966 

nnet 503 : -0.5096639 
nnet mean 503 : -0.4186379 
nnet RMSE 503 : 0.1519529 


s: 504 
logit 504 : -0.4655058 
logit mean 504 : -0.4394459 
logit RMSE 504 : 0.07046626 

boosting 504 : -0.5290988 
boosting mean 504 : -0.4837625 
boosting RMSE 504 : 0.1396033 

forest 504 : -0.4778826 
forest mean 504 : -0.3924576 
forest RMSE 504 : 0.0521734 

nnet 504 : -0.507638 
nnet mean 504 : -0.4188145 
nnet RMSE 504 : 0.1518777 


s: 505 
logit 505 : -0.3967925 
logit mean 505 : -0.4393614 
logit RMSE 505 : 0.0703966 

boosting 505 : -0.470253 
boosting mean 505 : -0.4837357 
boosting RMSE 505 : 0.1395 

forest 505 : -0.3683478 
forest mean 505 : -0.3924099 
forest RMSE 505 : 0.05214075 

nnet 505 : -0.3295769 
nnet mean 505 : -0.4186378 
nnet RMSE 505 : 0.1517597 


s: 506 
logit 506 : -0.5743109 
logit mean 506 : -0.4396281 
logit RMSE 506 : 0.07075264 

boosting 506 : -0.568024 
boosting mean 506 : -0.4839023 
boosting RMSE 506 : 0.1395621 

forest 506 : -0.4774903 
forest mean 506 : -0.392578 
forest RMSE 506 : 0.05220299 

nnet 506 : -0.3872853 
nnet mean 506 : -0.4185759 
nnet RMSE 506 : 0.1516107 


s: 507 
logit 507 : -0.3734859 
logit mean 507 : -0.4394977 
logit RMSE 507 : 0.07069264 

boosting 507 : -0.3235461 
boosting mean 507 : -0.483586 
boosting RMSE 507 : 0.1394658 

forest 507 : -0.3649084 
forest mean 507 : -0.3925234 
forest RMSE 507 : 0.05217476 

nnet 507 : -0.5994036 
nnet mean 507 : -0.4189325 
nnet RMSE 507 : 0.1517198 


s: 508 
logit 508 : -0.3011597 
logit mean 508 : -0.4392254 
logit RMSE 508 : 0.07075905 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 508 : -0.4979541 
boosting mean 508 : -0.4836143 
boosting RMSE 508 : 0.1393962 

forest 508 : -0.4059318 
forest mean 508 : -0.3925498 
forest RMSE 508 : 0.05212405 

nnet 508 : -0.6339369 
nnet mean 508 : -0.4193558 
nnet RMSE 508 : 0.1519253 


s: 509 
logit 509 : -0.3362173 
logit mean 509 : -0.439023 
logit RMSE 509 : 0.07074602 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 509 : -0.3710351 
boosting mean 509 : -0.4833931 
boosting RMSE 509 : 0.1392651 

forest 509 : -0.4036162 
forest mean 509 : -0.3925716 
forest RMSE 509 : 0.05207307 

nnet 509 : -0.375648 
nnet mean 509 : -0.4192699 
nnet RMSE 509 : 0.1517798 


s: 510 
logit 510 : -0.494293 
logit mean 510 : -0.4391314 
logit RMSE 510 : 0.07079985 

boosting 510 : -0.3080057 
boosting mean 510 : -0.4830493 
boosting RMSE 510 : 0.1391881 

forest 510 : -0.4332524 
forest mean 510 : -0.3926513 
forest RMSE 510 : 0.05204282 

nnet 510 : -0.473403 
nnet mean 510 : -0.419376 
nnet RMSE 510 : 0.1516658 


s: 511 
logit 511 : -0.3495274 
logit mean 511 : -0.438956 
logit RMSE 511 : 0.07076577 

boosting 511 : -0.5343274 
boosting mean 511 : -0.4831496 
boosting RMSE 511 : 0.1391788 

forest 511 : -0.4236358 
forest mean 511 : -0.392712 
forest RMSE 511 : 0.05200239 

nnet 511 : -0.4837915 
nnet mean 511 : -0.4195021 
nnet RMSE 511 : 0.1515627 


s: 512 
logit 512 : -0.3714914 
logit mean 512 : -0.4388242 
logit RMSE 512 : 0.07070786 

boosting 512 : -0.5471918 
boosting mean 512 : -0.4832747 
boosting RMSE 512 : 0.1391949 

forest 512 : -0.3623473 
forest mean 512 : -0.3926527 
forest RMSE 512 : 0.05197822 

nnet 512 : -0.3666922 
nnet mean 512 : -0.4193989 
nnet RMSE 512 : 0.1514217 


s: 513 
logit 513 : -0.4722736 
logit mean 513 : -0.4388895 
logit RMSE 513 : 0.07071094 

boosting 513 : -0.5125116 
boosting mean 513 : -0.4833317 
boosting RMSE 513 : 0.1391479 

forest 513 : -0.3287371 
forest mean 513 : -0.3925281 
forest RMSE 513 : 0.05202277 

nnet 513 : -0.2735140 
nnet mean 513 : -0.4191146 
nnet RMSE 513 : 0.1513771 


s: 514 
logit 514 : -0.4259892 
logit mean 514 : -0.4388644 
logit RMSE 514 : 0.07065143 

boosting 514 : -0.4449564 
boosting mean 514 : -0.483257 
boosting RMSE 514 : 0.1390266 

forest 514 : -0.4095035 
forest mean 514 : -0.3925611 
forest RMSE 514 : 0.05197383 

nnet 514 : -0.5201806 
nnet mean 514 : -0.4193112 
nnet RMSE 514 : 0.1513227 


s: 515 
logit 515 : -0.3946218 
logit mean 515 : -0.4387784 
logit RMSE 515 : 0.0705832 

boosting 515 : -0.3030769 
boosting mean 515 : -0.4829072 
boosting RMSE 515 : 0.1389572 

forest 515 : -0.3395445 
forest mean 515 : -0.3924581 
forest RMSE 515 : 0.05199164 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 515 : -0.1978497 
nnet mean 515 : -0.4188812 
nnet RMSE 515 : 0.1514379 


s: 516 
logit 516 : -0.3679838 
logit mean 516 : -0.4386412 
logit RMSE 516 : 0.07052885 

boosting 516 : -0.4289725 
boosting mean 516 : -0.4828026 
boosting RMSE 516 : 0.1388283 

forest 516 : -0.3678547 
forest mean 516 : -0.3924105 
forest RMSE 516 : 0.05196051 

nnet 516 : -0.4570064 
nnet mean 516 : -0.4189551 
nnet RMSE 516 : 0.1513119 


s: 517 
logit 517 : -0.4529117 
logit mean 517 : -0.4386688 
logit RMSE 517 : 0.07049903 

boosting 517 : -0.7748979 
boosting mean 517 : -0.4833676 
boosting RMSE 517 : 0.1396706 

forest 517 : -0.3927302 
forest mean 517 : -0.3924111 
forest RMSE 517 : 0.05191122 

nnet 517 : -0.3024970 
nnet mean 517 : -0.4187298 
nnet RMSE 517 : 0.1512263 


s: 518 
logit 518 : -0.4768441 
logit mean 518 : -0.4387425 
logit RMSE 518 : 0.07051183 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 518 : -0.4467726 
boosting mean 518 : -0.483297 
boosting RMSE 518 : 0.1395509 

forest 518 : -0.5222174 
forest mean 518 : -0.3926617 
forest RMSE 518 : 0.05213836 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 518 : -0.2436562 
nnet mean 518 : -0.4183918 
nnet RMSE 518 : 0.1512363 


s: 519 
logit 519 : -0.4316116 
logit mean 519 : -0.4387288 
logit RMSE 519 : 0.07045753 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 519 : -0.4720797 
boosting mean 519 : -0.4832753 
boosting RMSE 519 : 0.1394522 

forest 519 : -0.3970742 
forest mean 519 : -0.3926702 
forest RMSE 519 : 0.05208826 

nnet 519 : -0.2617245 
nnet mean 519 : -0.41809 
nnet RMSE 519 : 0.1512124 


s: 520 
logit 520 : -0.4969498 
logit mean 520 : -0.4388408 
logit RMSE 520 : 0.07051803 

boosting 520 : -0.5298268 
boosting mean 520 : -0.4833649 
boosting RMSE 520 : 0.1394344 

forest 520 : -0.3679354 
forest mean 520 : -0.3926226 
forest RMSE 520 : 0.05205715 

nnet 520 : -0.4435562 
nnet mean 520 : -0.4181389 
nnet RMSE 520 : 0.1510791 


s: 521 
logit 521 : -0.3456758 
logit mean 521 : -0.4386620 
logit RMSE 521 : 0.07049051 

boosting 521 : -0.3817398 
boosting mean 521 : -0.4831698 
boosting RMSE 521 : 0.1393028 

forest 521 : -0.4609028 
forest mean 521 : -0.3927537 
forest RMSE 521 : 0.05207557 

nnet 521 : -0.4721924 
nnet mean 521 : -0.4182427 
nnet RMSE 521 : 0.1509671 


s: 522 
logit 522 : -0.5343514 
logit mean 522 : -0.4388453 
logit RMSE 522 : 0.07066804 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 522 : -0.5466442 
boosting mean 522 : -0.4832914 
boosting RMSE 522 : 0.1393172 

forest 522 : -0.4493528 
forest mean 522 : -0.3928621 
forest RMSE 522 : 0.05207049 

nnet 522 : -0.3247586 
nnet mean 522 : -0.4180636 
nnet RMSE 522 : 0.1508584 


s: 523 
logit 523 : -0.3733565 
logit mean 523 : -0.4387200 
logit RMSE 523 : 0.07061006 

boosting 523 : -0.4199997 
boosting mean 523 : -0.4831704 
boosting RMSE 523 : 0.1391867 

forest 523 : -0.4455422 
forest mean 523 : -0.3929628 
forest RMSE 523 : 0.05205878 

nnet 523 : -0.6197426 
nnet mean 523 : -0.4184492 
nnet RMSE 523 : 0.1510201 


s: 524 
logit 524 : -0.4173229 
logit mean 524 : -0.4386792 
logit RMSE 524 : 0.07054671 

boosting 524 : -0.313826 
boosting mean 524 : -0.4828472 
boosting RMSE 524 : 0.1391048 

forest 524 : -0.4532983 
forest mean 524 : -0.393078 
forest RMSE 524 : 0.05206118 

nnet 524 : -0.7812235 
nnet mean 524 : -0.4191415 
nnet RMSE 524 : 0.1517923 


s: 525 
logit 525 : -0.5389919 
logit mean 525 : -0.4388703 
logit RMSE 525 : 0.07074006 

boosting 525 : -0.3979758 
boosting mean 525 : -0.4826856 
boosting RMSE 525 : 0.1389723 

forest 525 : -0.4164868 
forest mean 525 : -0.3931226 
forest RMSE 525 : 0.05201655 

nnet 525 : -0.4453978 
nnet mean 525 : -0.4191915 
nnet RMSE 525 : 0.1516606 


s: 526 
logit 526 : -0.5139537 
logit mean 526 : -0.439013 
logit RMSE 526 : 0.07084723 

boosting 526 : -0.366604 
boosting mean 526 : -0.4824649 
boosting RMSE 526 : 0.1388477 

forest 526 : -0.388427 
forest mean 526 : -0.3931136 
forest RMSE 526 : 0.05196953 

nnet 526 : -0.4301462 
nnet mean 526 : -0.4192124 
nnet RMSE 526 : 0.1515221 


s: 527 
logit 527 : -0.3028509 
logit mean 527 : -0.4387547 
logit RMSE 527 : 0.07090638 

boosting 527 : -0.4183906 
boosting mean 527 : -0.4823433 
boosting RMSE 527 : 0.1387183 

forest 527 : -0.2805064 
forest mean 527 : -0.3929000 
forest RMSE 527 : 0.05218047 

nnet 527 : -0.4905831 
nnet mean 527 : -0.4193478 
nnet RMSE 527 : 0.1514296 


s: 528 
logit 528 : -0.3642487 
logit mean 528 : -0.4386135 
logit RMSE 528 : 0.07085628 

boosting 528 : -0.2961435 
boosting mean 528 : -0.4819906 
boosting RMSE 528 : 0.1386605 

forest 528 : -0.3635708 
forest mean 528 : -0.3928444 
forest RMSE 528 : 0.05215513 

nnet 528 : -0.429971 
nnet mean 528 : -0.4193679 
nnet RMSE 528 : 0.1512918 


s: 529 
logit 529 : -0.4629437 
logit mean 529 : -0.4386595 
logit RMSE 529 : 0.07084216 

boosting 529 : -0.3922514 
boosting mean 529 : -0.481821 
boosting RMSE 529 : 0.1385298 

forest 529 : -0.3762351 
forest mean 529 : -0.392813 
forest RMSE 529 : 0.05211606 

nnet 529 : -0.2043906 
nnet mean 529 : -0.4189615 
nnet RMSE 529 : 0.1513878 


s: 530 
logit 530 : -0.465758 
logit mean 530 : -0.4387107 
logit RMSE 530 : 0.07083291 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 530 : -0.4840837 
boosting mean 530 : -0.4818253 
boosting RMSE 530 : 0.1384472 

forest 530 : -0.4664534 
forest mean 530 : -0.3929520 
forest RMSE 530 : 0.05214682 

nnet 530 : -0.5423321 
nnet mean 530 : -0.4191943 
nnet RMSE 530 : 0.1513712 


s: 531 
logit 531 : -0.4269192 
logit mean 531 : -0.4386885 
logit RMSE 531 : 0.07077582 

boosting 531 : -0.5702908 
boosting mean 531 : -0.4819919 
boosting RMSE 531 : 0.1385141 

forest 531 : -0.4345556 
forest mean 531 : -0.3930303 
forest RMSE 531 : 0.05211927 

nnet 531 : -0.4202788 
nnet mean 531 : -0.4191964 
nnet RMSE 531 : 0.1512312 


s: 532 
logit 532 : -0.4496338 
logit mean 532 : -0.438709 
logit RMSE 532 : 0.07074201 

boosting 532 : -0.5742215 
boosting mean 532 : -0.4821652 
boosting RMSE 532 : 0.1385898 

forest 532 : -0.4478178 
forest mean 532 : -0.3931333 
forest RMSE 532 : 0.05211152 

nnet 532 : -0.4558409 
nnet mean 532 : -0.4192652 
nnet RMSE 532 : 0.1511084 


s: 533 
logit 533 : -0.4182363 
logit mean 533 : -0.4386706 
logit RMSE 533 : 0.07068003 

boosting 533 : -0.5405802 
boosting mean 533 : -0.4822748 
boosting RMSE 533 : 0.1385936 

forest 533 : -0.3852422 
forest mean 533 : -0.3931185 
forest RMSE 533 : 0.05206654 

nnet 533 : -0.2904481 
nnet mean 533 : -0.4190235 
nnet RMSE 533 : 0.1510411 


s: 534 
logit 534 : -0.3961982 
logit mean 534 : -0.4385911 
logit RMSE 534 : 0.07061401 

boosting 534 : -0.3376428 
boosting mean 534 : -0.482004 
boosting RMSE 534 : 0.1384901 

forest 534 : -0.2948275 
forest mean 534 : -0.3929344 
forest RMSE 534 : 0.05221649 

nnet 534 : -0.4030808 
nnet mean 534 : -0.4189937 
nnet RMSE 534 : 0.1508997 


s: 535 
logit 535 : -0.3832546 
logit mean 535 : -0.4384877 
logit RMSE 535 : 0.0705517 

boosting 535 : -0.3355854 
boosting mean 535 : -0.4817303 
boosting RMSE 535 : 0.1383886 

forest 535 : -0.3751925 
forest mean 535 : -0.3929013 
forest RMSE 535 : 0.05217869 

nnet 535 : -0.3339319 
nnet mean 535 : -0.4188347 
nnet RMSE 535 : 0.1507857 


s: 536 
logit 536 : -0.4205664 
logit mean 536 : -0.4384542 
logit RMSE 536 : 0.07049145 

boosting 536 : -0.4722721 
boosting mean 536 : -0.4817127 
boosting RMSE 536 : 0.1382947 

forest 536 : -0.2877484 
forest mean 536 : -0.3927051 
forest RMSE 536 : 0.05235498 

nnet 536 : -0.4487539 
nnet mean 536 : -0.4188905 
nnet RMSE 536 : 0.1506597 


s: 537 
logit 537 : -0.4710681 
logit mean 537 : -0.4385150 
logit RMSE 537 : 0.07049253 

boosting 537 : -0.5181991 
boosting mean 537 : -0.4817806 
boosting RMSE 537 : 0.1382600 

forest 537 : -0.3073253 
forest mean 537 : -0.3925461 
forest RMSE 537 : 0.05245888 

nnet 537 : -0.2410850 
nnet mean 537 : -0.4185594 
nnet RMSE 537 : 0.1506754 


s: 538 
logit 538 : -0.4193112 
logit mean 538 : -0.4384793 
logit RMSE 538 : 0.07043191 

boosting 538 : -0.4006139 
boosting mean 538 : -0.4816297 
boosting RMSE 538 : 0.1381314 

forest 538 : -0.4628496 
forest mean 538 : -0.3926768 
forest RMSE 538 : 0.0524801 

nnet 538 : -0.06106818 
nnet mean 538 : -0.4178949 
nnet RMSE 538 : 0.1512429 


s: 539 
logit 539 : -0.5291648 
logit mean 539 : -0.4386475 
logit RMSE 539 : 0.07058614 

boosting 539 : -0.4244061 
boosting mean 539 : -0.4815236 
boosting RMSE 539 : 0.1380072 

forest 539 : -0.3875387 
forest mean 539 : -0.3926672 
forest RMSE 539 : 0.05243414 

nnet 539 : -0.4103351 
nnet mean 539 : -0.4178809 
nnet RMSE 539 : 0.1511032 


s: 540 
logit 540 : -0.4129933 
logit mean 540 : -0.4386 
logit RMSE 540 : 0.07052297 

boosting 540 : -0.430644 
boosting mean 540 : -0.4814293 
boosting RMSE 540 : 0.1378857 

forest 540 : -0.3597130 
forest mean 540 : -0.3926062 
forest RMSE 540 : 0.05241425 

nnet 540 : -0.3678947 
nnet mean 540 : -0.4177883 
nnet RMSE 540 : 0.1509695 


s: 541 
logit 541 : -0.2867004 
logit mean 541 : -0.4383192 
logit RMSE 541 : 0.07062594 

boosting 541 : -0.3022011 
boosting mean 541 : -0.4810981 
boosting RMSE 541 : 0.1378224 

forest 541 : -0.3154337 
forest mean 541 : -0.3924635 
forest RMSE 541 : 0.05249185 

nnet 541 : -0.4522869 
nnet mean 541 : -0.4178521 
nnet RMSE 541 : 0.1508467 


s: 542 
logit 542 : -0.4580072 
logit mean 542 : -0.4383555 
logit RMSE 542 : 0.07060474 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 542 : -0.5164511 
boosting mean 542 : -0.4811633 
boosting RMSE 542 : 0.1377860 

forest 542 : -0.3460762 
forest mean 542 : -0.3923780 
forest RMSE 542 : 0.05249453 

nnet 542 : -0.4906516 
nnet mean 542 : -0.4179864 
nnet RMSE 542 : 0.1507578 


s: 543 
logit 543 : -0.4220932 
logit mean 543 : -0.4383256 
logit RMSE 543 : 0.07054607 

boosting 543 : -0.5309735 
boosting mean 543 : -0.481255 
boosting RMSE 543 : 0.1377737 

forest 543 : -0.3851289 
forest mean 543 : -0.3923646 
forest RMSE 543 : 0.05245005 

nnet 543 : -0.4782834 
nnet mean 543 : -0.4180975 
nnet RMSE 543 : 0.1506563 


s: 544 
logit 544 : -0.3915922 
logit mean 544 : -0.4382397 
logit RMSE 544 : 0.07048212 

boosting 544 : -0.6444732 
boosting mean 544 : -0.481555 
boosting RMSE 544 : 0.1380456 

forest 544 : -0.3170194 
forest mean 544 : -0.3922261 
forest RMSE 544 : 0.05252246 

nnet 544 : -0.4347366 
nnet mean 544 : -0.4181280 
nnet RMSE 544 : 0.1505252 


s: 545 
logit 545 : -0.4234545 
logit mean 545 : -0.4382126 
logit RMSE 545 : 0.07042459 

boosting 545 : -0.455287 
boosting mean 545 : -0.4815068 
boosting RMSE 545 : 0.1379392 

forest 545 : -0.356807 
forest mean 545 : -0.3921611 
forest RMSE 545 : 0.05250686 

nnet 545 : -0.3566326 
nnet mean 545 : -0.4180152 
nnet RMSE 545 : 0.1503985 


s: 546 
logit 546 : -0.3801573 
logit mean 546 : -0.4381062 
logit RMSE 546 : 0.0703652 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 546 : -0.4774636 
boosting mean 546 : -0.4814994 
boosting RMSE 546 : 0.1378527 

forest 546 : -0.4301864 
forest mean 546 : -0.3922308 
forest RMSE 546 : 0.05247465 

nnet 546 : -0.3121014 
nnet mean 546 : -0.4178212 
nnet RMSE 546 : 0.1503078 


s: 547 
logit 547 : -0.4238483 
logit mean 547 : -0.4380802 
logit RMSE 547 : 0.07030824 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 547 : -0.3009706 
boosting mean 547 : -0.4811694 
boosting RMSE 547 : 0.1377917 

forest 547 : -0.3362854 
forest mean 547 : -0.3921285 
forest RMSE 547 : 0.0524974 

nnet 547 : -0.3785248 
nnet mean 547 : -0.4177494 
nnet RMSE 547 : 0.1501731 


s: 548 
logit 548 : -0.3361555 
logit mean 548 : -0.4378942 
logit RMSE 548 : 0.07029699 

boosting 548 : -0.4528197 
boosting mean 548 : -0.4811177 
boosting RMSE 548 : 0.1376844 

forest 548 : -0.4005142 
forest mean 548 : -0.3921438 
forest RMSE 548 : 0.05244948 

nnet 548 : -0.2859644 
nnet mean 548 : -0.4175089 
nnet RMSE 548 : 0.1501151 


s: 549 
logit 549 : -0.4134159 
logit mean 549 : -0.4378496 
logit RMSE 549 : 0.07023527 

boosting 549 : -0.5893909 
boosting mean 549 : -0.4813149 
boosting RMSE 549 : 0.1377962 

forest 549 : -0.3550405 
forest mean 549 : -0.3920762 
forest RMSE 549 : 0.05243681 

nnet 549 : -0.518036 
nnet mean 549 : -0.417692 
nnet RMSE 549 : 0.1500629 


s: 550 
logit 550 : -0.3789244 
logit mean 550 : -0.4377425 
logit RMSE 550 : 0.07017714 

boosting 550 : -0.7272581 
boosting mean 550 : -0.4817621 
boosting RMSE 550 : 0.1383763 

forest 550 : -0.5041328 
forest mean 550 : -0.3922799 
forest RMSE 550 : 0.05257695 

nnet 550 : -0.3882393 
nnet mean 550 : -0.4176385 
nnet RMSE 550 : 0.1499273 


s: 551 
logit 551 : -0.478545 
logit mean 551 : -0.4378165 
logit RMSE 551 : 0.07019323 

boosting 551 : -0.6713933 
boosting mean 551 : -0.4821062 
boosting RMSE 551 : 0.1387333 

forest 551 : -0.4212568 
forest mean 551 : -0.3923325 
forest RMSE 551 : 0.05253702 

nnet 551 : -0.823854 
nnet mean 551 : -0.4183757 
nnet RMSE 551 : 0.1508756 


s: 552 
logit 552 : -0.4481859 
logit mean 552 : -0.4378353 
logit RMSE 552 : 0.07015961 

boosting 552 : -0.5081089 
boosting mean 552 : -0.4821533 
boosting RMSE 552 : 0.1386839 

forest 552 : -0.4288855 
forest mean 552 : -0.3923988 
forest RMSE 552 : 0.05250381 

nnet 552 : -0.5311731 
nnet mean 552 : -0.4185800 
nnet RMSE 552 : 0.1508422 


s: 553 
logit 553 : -0.527053 
logit mean 553 : -0.4379966 
logit RMSE 553 : 0.07030405 

boosting 553 : -0.5575521 
boosting mean 553 : -0.4822897 
boosting RMSE 553 : 0.1387203 

forest 553 : -0.3944403 
forest mean 553 : -0.3924024 
forest RMSE 553 : 0.05245685 

nnet 553 : -0.3136912 
nnet mean 553 : -0.4183904 
nnet RMSE 553 : 0.1507504 


s: 554 
logit 554 : -0.3852722 
logit mean 554 : -0.4379015 
logit RMSE 554 : 0.07024336 

boosting 554 : -0.4491351 
boosting mean 554 : -0.4822298 
boosting RMSE 554 : 0.1386108 

forest 554 : -0.461582 
forest mean 554 : -0.3925273 
forest RMSE 554 : 0.05247475 

nnet 554 : -0.4109494 
nnet mean 554 : -0.4183769 
nnet RMSE 554 : 0.1506150 


s: 555 
logit 555 : -0.4655631 
logit mean 555 : -0.4379513 
logit RMSE 555 : 0.07023521 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 555 : -0.6742493 
boosting mean 555 : -0.4825758 
boosting RMSE 555 : 0.1389743 

forest 555 : -0.3800182 
forest mean 555 : -0.3925048 
forest RMSE 555 : 0.05243431 

nnet 555 : -0.4540064 
nnet mean 555 : -0.4184411 
nnet RMSE 555 : 0.1504968 


s: 556 
logit 556 : -0.397547 
logit mean 556 : -0.4378786 
logit RMSE 556 : 0.0701721 

boosting 556 : -0.4698778 
boosting mean 556 : -0.482553 
boosting RMSE 556 : 0.1388809 

forest 556 : -0.3817534 
forest mean 556 : -0.3924854 
forest RMSE 556 : 0.05239286 

nnet 556 : -0.1563014 
nnet mean 556 : -0.4179697 
nnet RMSE 556 : 0.1507161 


s: 557 
logit 557 : -0.3511255 
logit mean 557 : -0.4377229 
logit RMSE 557 : 0.07013966 

boosting 557 : -0.2994488 
boosting mean 557 : -0.4822242 
boosting RMSE 557 : 0.1388216 

forest 557 : -0.3571721 
forest mean 557 : -0.3924220 
forest RMSE 557 : 0.05237725 

nnet 557 : -0.5382597 
nnet mean 557 : -0.4181856 
nnet RMSE 557 : 0.1506947 


s: 558 
logit 558 : -0.4536595 
logit mean 558 : -0.4377514 
logit RMSE 558 : 0.07011359 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 558 : -0.534144 
boosting mean 558 : -0.4823173 
boosting RMSE 558 : 0.1388133 

forest 558 : -0.3209713 
forest mean 558 : -0.392294 
forest RMSE 558 : 0.05243713 

nnet 558 : -0.3740996 
nnet mean 558 : -0.4181066 
nnet RMSE 558 : 0.1505636 


s: 559 
logit 559 : -0.4674092 
logit mean 559 : -0.4378045 
logit RMSE 559 : 0.07010884 

boosting 559 : -0.4047595 
boosting mean 559 : -0.4821785 
boosting RMSE 559 : 0.1386892 

forest 559 : -0.4307427 
forest mean 559 : -0.3923628 
forest RMSE 559 : 0.05240634 

nnet 559 : -0.3110138 
nnet mean 559 : -0.417915 
nnet RMSE 559 : 0.1504759 


s: 560 
logit 560 : -0.3808059 
logit mean 560 : -0.4377027 
logit RMSE 560 : 0.07005091 

boosting 560 : -0.5573423 
boosting mean 560 : -0.4823128 
boosting RMSE 560 : 0.1387248 

forest 560 : -0.3634138 
forest mean 560 : -0.3923111 
forest RMSE 560 : 0.05238235 

nnet 560 : -0.3754615 
nnet mean 560 : -0.4178392 
nnet RMSE 560 : 0.1503451 


s: 561 
logit 561 : -0.4512925 
logit mean 561 : -0.4377269 
logit RMSE 561 : 0.07002195 

boosting 561 : -0.514317 
boosting mean 561 : -0.4823698 
boosting RMSE 561 : 0.1386851 

forest 561 : -0.3964389 
forest mean 561 : -0.3923184 
forest RMSE 561 : 0.05233585 

nnet 561 : -0.5778028 
nnet mean 561 : -0.4181244 
nnet RMSE 561 : 0.1503985 


s: 562 
logit 562 : -0.3019748 
logit mean 562 : -0.4374854 
logit RMSE 562 : 0.07008171 

boosting 562 : -0.6058192 
boosting mean 562 : -0.4825895 
boosting RMSE 562 : 0.1388334 

forest 562 : -0.3922538 
forest mean 562 : -0.3923183 
forest RMSE 562 : 0.05229029 

nnet 562 : -0.4454311 
nnet mean 562 : -0.4181730 
nnet RMSE 562 : 0.1502769 


s: 563 
logit 563 : -0.4184125 
logit mean 563 : -0.4374515 
logit RMSE 563 : 0.07002374 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 563 : -0.3817166 
boosting mean 563 : -0.4824103 
boosting RMSE 563 : 0.1387122 

forest 563 : -0.4265842 
forest mean 563 : -0.3923792 
forest RMSE 563 : 0.05225585 

nnet 563 : -0.39042 
nnet mean 563 : -0.4181237 
nnet RMSE 563 : 0.1501439 


s: 564 
logit 564 : -0.4909531 
logit mean 564 : -0.4375464 
logit RMSE 564 : 0.07006639 

boosting 564 : -0.5141783 
boosting mean 564 : -0.4824666 
boosting RMSE 564 : 0.1386725 

forest 564 : -0.3652511 
forest mean 564 : -0.3923311 
forest RMSE 564 : 0.05223 

nnet 564 : -0.1574250 
nnet mean 564 : -0.4176614 
nnet RMSE 564 : 0.1503581 


s: 565 
logit 565 : -0.4478161 
logit mean 565 : -0.4375645 
logit RMSE 565 : 0.07003325 

boosting 565 : -0.3895404 
boosting mean 565 : -0.4823022 
boosting RMSE 565 : 0.1385504 

forest 565 : -0.4165953 
forest mean 565 : -0.392374 
forest RMSE 565 : 0.05218843 

nnet 565 : -0.2672765 
nnet mean 565 : -0.4173953 
nnet RMSE 565 : 0.1503287 


s: 566 
logit 566 : -0.3542651 
logit mean 566 : -0.4374174 
logit RMSE 566 : 0.06999776 

boosting 566 : -0.5168835 
boosting mean 566 : -0.4823633 
boosting RMSE 566 : 0.1385152 

forest 566 : -0.3606874 
forest mean 566 : -0.3923181 
forest RMSE 566 : 0.05216848 

nnet 566 : -0.5800849 
nnet mean 566 : -0.4176827 
nnet RMSE 566 : 0.1503864 


s: 567 
logit 567 : -0.3975403 
logit mean 567 : -0.437347 
logit RMSE 567 : 0.06993608 

boosting 567 : -0.4634587 
boosting mean 567 : -0.4823299 
boosting RMSE 567 : 0.1384186 

forest 567 : -0.3458941 
forest mean 567 : -0.3922362 
forest RMSE 567 : 0.05217196 

nnet 567 : -0.2347334 
nnet mean 567 : -0.41736 
nnet RMSE 567 : 0.1504140 


s: 568 
logit 568 : -0.4093406 
logit mean 568 : -0.4372977 
logit RMSE 568 : 0.06987559 

boosting 568 : -0.7604213 
boosting mean 568 : -0.4828195 
boosting RMSE 568 : 0.1391211 

forest 568 : -0.3674406 
forest mean 568 : -0.3921925 
forest RMSE 568 : 0.05214391 

nnet 568 : -0.4350475 
nnet mean 568 : -0.4173912 
nnet RMSE 568 : 0.1502887 


s: 569 
logit 569 : -0.4519729 
logit mean 569 : -0.4373235 
logit RMSE 569 : 0.06984815 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 569 : -0.6463804 
boosting mean 569 : -0.483107 
boosting RMSE 569 : 0.1393820 

forest 569 : -0.4018418 
forest mean 569 : -0.3922095 
forest RMSE 569 : 0.05209813 

nnet 569 : -0.5415845 
nnet mean 569 : -0.4176094 
nnet RMSE 569 : 0.1502738 


s: 570 
logit 570 : -0.5893621 
logit mean 570 : -0.4375903 
logit RMSE 570 : 0.07023613 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 570 : -0.3154462 
boosting mean 570 : -0.4828128 
boosting RMSE 570 : 0.1393047 

forest 570 : -0.3675419 
forest mean 570 : -0.3921662 
forest RMSE 570 : 0.05207016 

nnet 570 : -0.6256962 
nnet mean 570 : -0.4179745 
nnet RMSE 570 : 0.1504393 


s: 571 
logit 571 : -0.4176156 
logit mean 571 : -0.4375553 
logit RMSE 571 : 0.07017847 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 571 : -0.3160804 
boosting mean 571 : -0.4825208 
boosting RMSE 571 : 0.139227 

forest 571 : -0.3907928 
forest mean 571 : -0.3921638 
forest RMSE 571 : 0.05202597 

nnet 571 : -0.2960348 
nnet mean 571 : -0.4177610 
nnet RMSE 571 : 0.1503704 


s: 572 
logit 572 : -0.4125724 
logit mean 572 : -0.4375116 
logit RMSE 572 : 0.07011907 

boosting 572 : -0.4025453 
boosting mean 572 : -0.482381 
boosting RMSE 572 : 0.1391053 

forest 572 : -0.3646922 
forest mean 572 : -0.3921158 
forest RMSE 572 : 0.05200144 

nnet 572 : -0.2105385 
nnet mean 572 : -0.4173987 
nnet RMSE 572 : 0.1504477 


s: 573 
logit 573 : -0.4206352 
logit mean 573 : -0.4374821 
logit RMSE 573 : 0.07006316 

boosting 573 : -0.6149343 
boosting mean 573 : -0.4826123 
boosting RMSE 573 : 0.1392736 

forest 573 : -0.3800028 
forest mean 573 : -0.3920946 
forest RMSE 573 : 0.05196275 

nnet 573 : -0.3099097 
nnet mean 573 : -0.4172111 
nnet RMSE 573 : 0.1503634 


s: 574 
logit 574 : -0.5320419 
logit mean 574 : -0.4376469 
logit RMSE 574 : 0.07021872 

boosting 574 : -0.4498588 
boosting mean 574 : -0.4825553 
boosting RMSE 574 : 0.1391678 

forest 574 : -0.3781649 
forest mean 574 : -0.3920704 
forest RMSE 574 : 0.05192547 

nnet 574 : -0.5671231 
nnet mean 574 : -0.4174723 
nnet RMSE 574 : 0.1503942 


s: 575 
logit 575 : -0.4019283 
logit mean 575 : -0.4375848 
logit RMSE 575 : 0.07015768 

boosting 575 : -0.5393733 
boosting mean 575 : -0.4826541 
boosting RMSE 575 : 0.1391681 

forest 575 : -0.4050785 
forest mean 575 : -0.392093 
forest RMSE 575 : 0.05188073 

nnet 575 : -0.6701484 
nnet mean 575 : -0.4179117 
nnet RMSE 575 : 0.1506851 


s: 576 
logit 576 : -0.5434632 
logit mean 576 : -0.4377686 
logit RMSE 576 : 0.07035117 

boosting 576 : -0.5507994 
boosting mean 576 : -0.4827724 
boosting RMSE 576 : 0.1391892 

forest 576 : -0.4521926 
forest mean 576 : -0.3921973 
forest RMSE 576 : 0.05188127 

nnet 576 : -0.1774308 
nnet mean 576 : -0.4174942 
nnet RMSE 576 : 0.1508396 


s: 577 
logit 577 : -0.4847439 
logit mean 577 : -0.43785 
logit RMSE 577 : 0.07037866 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 577 : -0.4690329 
boosting mean 577 : -0.4827486 
boosting RMSE 577 : 0.1390982 

forest 577 : -0.5023134 
forest mean 577 : -0.3923882 
forest RMSE 577 : 0.052011 

nnet 577 : -0.8016718 
nnet mean 577 : -0.41816 
nnet RMSE 577 : 0.1516337 


s: 578 
logit 578 : -0.4614021 
logit mean 578 : -0.4378907 
logit RMSE 578 : 0.07036412 

boosting 578 : -0.4725394 
boosting mean 578 : -0.4827309 
boosting RMSE 578 : 0.1390106 

forest 578 : -0.3594487 
forest mean 578 : -0.3923312 
forest RMSE 578 : 0.05199335 

nnet 578 : -0.3077071 
nnet mean 578 : -0.4179689 
nnet RMSE 578 : 0.1515511 


s: 579 
logit 579 : -0.5016854 
logit mean 579 : -0.4380009 
logit RMSE 579 : 0.07043022 

boosting 579 : -0.4082187 
boosting mean 579 : -0.4826022 
boosting RMSE 579 : 0.1388909 

forest 579 : -0.4628197 
forest mean 579 : -0.3924529 
forest RMSE 579 : 0.05201399 

nnet 579 : -0.4993996 
nnet mean 579 : -0.4181096 
nnet RMSE 579 : 0.1514765 


s: 580 
logit 580 : -0.4568734 
logit mean 580 : -0.4380335 
logit RMSE 580 : 0.0704091 

boosting 580 : -0.5767677 
boosting mean 580 : -0.4827646 
boosting RMSE 580 : 0.1389651 

forest 580 : -0.4422171 
forest mean 580 : -0.3925387 
forest RMSE 580 : 0.05199869 

nnet 580 : -0.3686193 
nnet mean 580 : -0.4180242 
nnet RMSE 580 : 0.1513515 


s: 581 
logit 581 : -0.4542941 
logit mean 581 : -0.4380614 
logit RMSE 581 : 0.07038453 

boosting 581 : -0.5088441 
boosting mean 581 : -0.4828095 
boosting RMSE 581 : 0.1389189 

forest 581 : -0.4242622 
forest mean 581 : -0.3925933 
forest RMSE 581 : 0.05196367 

nnet 581 : -0.2883317 
nnet mean 581 : -0.417801 
nnet RMSE 581 : 0.1512921 


s: 582 
logit 582 : -0.4711983 
logit mean 582 : -0.4381184 
logit RMSE 582 : 0.07038594 

boosting 582 : -0.6665035 
boosting mean 582 : -0.4831251 
boosting RMSE 582 : 0.1392384 

forest 582 : -0.4293348 
forest mean 582 : -0.3926565 
forest RMSE 582 : 0.05193325 

nnet 582 : -0.1565610 
nnet mean 582 : -0.4173521 
nnet RMSE 582 : 0.1514985 


s: 583 
logit 583 : -0.3440393 
logit mean 583 : -0.437957 
logit RMSE 583 : 0.07036373 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 583 : -0.2946342 
boosting mean 583 : -0.4828018 
boosting RMSE 583 : 0.1391873 

forest 583 : -0.3482031 
forest mean 583 : -0.3925802 
forest RMSE 583 : 0.05193301 

nnet 583 : -0.4171626 
nnet mean 583 : -0.4173518 
nnet RMSE 583 : 0.1513702 


s: 584 
logit 584 : -0.4505069 
logit mean 584 : -0.4379785 
logit RMSE 584 : 0.07033452 

boosting 584 : -0.4539734 
boosting mean 584 : -0.4827524 
boosting RMSE 584 : 0.1390860 

forest 584 : -0.3917669 
forest mean 584 : -0.3925788 
forest RMSE 584 : 0.05188965 

nnet 584 : -0.4546916 
nnet mean 584 : -0.4174158 
nnet RMSE 584 : 0.1512575 


s: 585 
logit 585 : -0.3796048 
logit mean 585 : -0.4378787 
logit RMSE 585 : 0.07027943 

boosting 585 : -0.4846998 
boosting mean 585 : -0.4827558 
boosting RMSE 585 : 0.1390112 

forest 585 : -0.496331 
forest mean 585 : -0.3927562 
forest RMSE 585 : 0.05199804 

nnet 585 : -0.3040569 
nnet mean 585 : -0.417222 
nnet RMSE 585 : 0.1511802 


s: 586 
logit 586 : -0.4831876 
logit mean 586 : -0.437956 
logit RMSE 586 : 0.07030348 

boosting 586 : -0.4624957 
boosting mean 586 : -0.4827212 
boosting RMSE 586 : 0.1389166 

forest 586 : -0.3997341 
forest mean 586 : -0.3927681 
forest RMSE 586 : 0.05195365 

nnet 586 : -0.5799086 
nnet mean 586 : -0.4174996 
nnet RMSE 586 : 0.1512339 


s: 587 
logit 587 : -0.5201877 
logit mean 587 : -0.4380961 
logit RMSE 587 : 0.07041852 

boosting 587 : -0.4809987 
boosting mean 587 : -0.4827182 
boosting RMSE 587 : 0.1388384 

forest 587 : -0.3830649 
forest mean 587 : -0.3927515 
forest RMSE 587 : 0.05191408 

nnet 587 : -0.4225953 
nnet mean 587 : -0.4175083 
nnet RMSE 587 : 0.1511079 


s: 588 
logit 588 : -0.4094891 
logit mean 588 : -0.4380475 
logit RMSE 588 : 0.0703597 

boosting 588 : -0.3598287 
boosting mean 588 : -0.4825092 
boosting RMSE 588 : 0.1387302 

forest 588 : -0.4004527 
forest mean 588 : -0.3927646 
forest RMSE 588 : 0.05186992 

nnet 588 : -0.5997217 
nnet mean 588 : -0.4178182 
nnet RMSE 588 : 0.1512038 


s: 589 
logit 589 : -0.424415 
logit mean 589 : -0.4380243 
logit RMSE 589 : 0.07030714 

boosting 589 : -0.4583571 
boosting mean 589 : -0.4824682 
boosting RMSE 589 : 0.1386333 

forest 589 : -0.3780167 
forest mean 589 : -0.3927396 
forest RMSE 589 : 0.05183379 

nnet 589 : -0.2362565 
nnet mean 589 : -0.4175099 
nnet RMSE 589 : 0.151226 


s: 590 
logit 590 : -0.401799 
logit mean 590 : -0.4379629 
logit RMSE 590 : 0.07024758 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 590 : -0.5660826 
boosting mean 590 : -0.48261 
boosting RMSE 590 : 0.1386844 

forest 590 : -0.3758732 
forest mean 590 : -0.392711 
forest RMSE 590 : 0.05179937 

nnet 590 : -0.4203359 
nnet mean 590 : -0.4175147 
nnet RMSE 590 : 0.1511001 


s: 591 
logit 591 : -0.4711593 
logit mean 591 : -0.4380191 
logit RMSE 591 : 0.07024913 

boosting 591 : -0.6278169 
boosting mean 591 : -0.4828557 
boosting RMSE 591 : 0.1388835 

forest 591 : -0.3924454 
forest mean 591 : -0.3927106 
forest RMSE 591 : 0.05175646 

nnet 591 : -0.738583 
nnet mean 591 : -0.418058 
nnet RMSE 591 : 0.1516133 


s: 592 
logit 592 : -0.4426818 
logit mean 592 : -0.438027 
logit RMSE 592 : 0.07021169 

boosting 592 : -0.4561152 
boosting mean 592 : -0.4828105 
boosting RMSE 592 : 0.1387853 

forest 592 : -0.403469 
forest mean 592 : -0.3927287 
forest RMSE 592 : 0.05171292 

nnet 592 : -0.687836 
nnet mean 592 : -0.4185137 
nnet RMSE 592 : 0.1519464 


s: 593 
logit 593 : -0.4557899 
logit mean 593 : -0.4380569 
logit RMSE 593 : 0.07018986 

boosting 593 : -0.4618438 
boosting mean 593 : -0.4827751 
boosting RMSE 593 : 0.1386915 

forest 593 : -0.3730504 
forest mean 593 : -0.3926956 
forest RMSE 593 : 0.05168115 

nnet 593 : -0.3558633 
nnet mean 593 : -0.418408 
nnet RMSE 593 : 0.1518290 


s: 594 
logit 594 : -0.445542 
logit mean 594 : -0.4380695 
logit RMSE 594 : 0.07015565 

boosting 594 : -0.4123890 
boosting mean 594 : -0.4826566 
boosting RMSE 594 : 0.1385757 

forest 594 : -0.3064527 
forest mean 594 : -0.3925504 
forest RMSE 594 : 0.05178009 

nnet 594 : -0.4658546 
nnet mean 594 : -0.4184879 
nnet RMSE 594 : 0.1517252 


s: 595 
logit 595 : -0.4432752 
logit mean 595 : -0.4380783 
logit RMSE 595 : 0.07011911 

boosting 595 : -0.4152997 
boosting mean 595 : -0.4825434 
boosting RMSE 595 : 0.1384606 

forest 595 : -0.463352 
forest mean 595 : -0.3926694 
forest RMSE 595 : 0.0518017 

nnet 595 : -0.6030549 
nnet mean 595 : -0.4187981 
nnet RMSE 595 : 0.1518261 


s: 596 
logit 596 : -0.4718611 
logit mean 596 : -0.438135 
logit RMSE 596 : 0.07012207 

boosting 596 : -0.597341 
boosting mean 596 : -0.482736 
boosting RMSE 596 : 0.1385803 

forest 596 : -0.3822626 
forest mean 596 : -0.3926519 
forest RMSE 596 : 0.05176333 

nnet 596 : -0.519634 
nnet mean 596 : -0.4189673 
nnet RMSE 596 : 0.1517778 


s: 597 
logit 597 : -0.5677684 
logit mean 597 : -0.4383521 
logit RMSE 597 : 0.07039897 

boosting 597 : -0.4005945 
boosting mean 597 : -0.4825985 
boosting RMSE 597 : 0.1384642 

forest 597 : -0.4760643 
forest mean 597 : -0.3927916 
forest RMSE 597 : 0.05181356 

nnet 597 : -0.5054878 
nnet mean 597 : -0.4191122 
nnet RMSE 597 : 0.1517120 


s: 598 
logit 598 : -0.4397098 
logit mean 598 : -0.4383544 
logit RMSE 598 : 0.07035882 

boosting 598 : -0.3649418 
boosting mean 598 : -0.4824017 
boosting RMSE 598 : 0.1383558 

forest 598 : -0.4582768 
forest mean 598 : -0.3929011 
forest RMSE 598 : 0.05182504 

nnet 598 : -0.3363900 
nnet mean 598 : -0.4189739 
nnet RMSE 598 : 0.1516075 


s: 599 
logit 599 : -0.4155036 
logit mean 599 : -0.4383162 
logit RMSE 599 : 0.07030292 

boosting 599 : -0.4635305 
boosting mean 599 : -0.4823702 
boosting RMSE 599 : 0.1382647 

forest 599 : -0.3776234 
forest mean 599 : -0.3928756 
forest RMSE 599 : 0.05178984 

nnet 599 : -0.5740092 
nnet mean 599 : -0.4192327 
nnet RMSE 599 : 0.1516476 


s: 600 
logit 600 : -0.4954493 
logit mean 600 : -0.4384114 
logit RMSE 600 : 0.07035231 

boosting 600 : -0.5542648 
boosting mean 600 : -0.48249 
boosting RMSE 600 : 0.1382929 

forest 600 : -0.3703296 
forest mean 600 : -0.392838 
forest RMSE 600 : 0.05176084 

nnet 600 : -0.2222996 
nnet mean 600 : -0.4189045 
nnet RMSE 600 : 0.1516948 


s: 601 
logit 601 : -0.3951762 
logit mean 601 : -0.4383395 
logit RMSE 601 : 0.07029403 

boosting 601 : -0.5690453 
boosting mean 601 : -0.482634 
boosting RMSE 601 : 0.1383497 

forest 601 : -0.3640105 
forest mean 601 : -0.3927901 
forest RMSE 601 : 0.05173859 

nnet 601 : -0.3711777 
nnet mean 601 : -0.4188251 
nnet RMSE 601 : 0.1515731 


s: 602 
logit 602 : -0.4244262 
logit mean 602 : -0.4383164 
logit RMSE 602 : 0.07024268 

boosting 602 : -0.1924369 
boosting mean 602 : -0.482152 
boosting RMSE 602 : 0.1384934 

forest 602 : -0.3739993 
forest mean 602 : -0.3927589 
forest RMSE 602 : 0.05170646 

nnet 602 : -0.5832261 
nnet mean 602 : -0.4190982 
nnet RMSE 602 : 0.1516311 


s: 603 
logit 603 : -0.492141 
logit mean 603 : -0.4384057 
logit RMSE 603 : 0.07028465 

boosting 603 : -0.5119544 
boosting mean 603 : -0.4822014 
boosting RMSE 603 : 0.1384536 

forest 603 : -0.3928509 
forest mean 603 : -0.392759 
forest RMSE 603 : 0.05166439 

nnet 603 : -0.4555943 
nnet mean 603 : -0.4191587 
nnet RMSE 603 : 0.1515223 


s: 604 
logit 604 : -0.383388 
logit mean 604 : -0.4383146 
logit RMSE 604 : 0.07022969 

boosting 604 : -0.5544173 
boosting mean 604 : -0.482321 
boosting RMSE 604 : 0.1384815 

forest 604 : -0.2857778 
forest mean 604 : -0.3925819 
forest RMSE 604 : 0.0518304 

nnet 604 : -0.1081307 
nnet mean 604 : -0.4186437 
nnet RMSE 604 : 0.1518619 


s: 605 
logit 605 : -0.3852499 
logit mean 605 : -0.4382269 
logit RMSE 605 : 0.07017419 

boosting 605 : -0.6382178 
boosting mean 605 : -0.4825787 
boosting RMSE 605 : 0.1387056 

forest 605 : -0.4095224 
forest mean 605 : -0.3926099 
forest RMSE 605 : 0.05178899 

nnet 605 : -0.5907303 
nnet mean 605 : -0.4189282 
nnet RMSE 605 : 0.1519343 


s: 606 
logit 606 : -0.4609051 
logit mean 606 : -0.4382643 
logit RMSE 606 : 0.0701599 

boosting 606 : -0.4706322 
boosting mean 606 : -0.4825589 
boosting RMSE 606 : 0.1386208 

forest 606 : -0.3162313 
forest mean 606 : -0.3924839 
forest RMSE 606 : 0.05185801 

nnet 606 : -0.394097 
nnet mean 606 : -0.4188872 
nnet RMSE 606 : 0.1518091 


s: 607 
logit 607 : -0.4354538 
logit mean 607 : -0.4382597 
logit RMSE 607 : 0.07011685 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 607 : -0.5683011 
boosting mean 607 : -0.4827002 
boosting RMSE 607 : 0.1386749 

forest 607 : -0.3892884 
forest mean 607 : -0.3924786 
forest RMSE 607 : 0.0518171 

nnet 607 : -0.6523397 
nnet mean 607 : -0.4192718 
nnet RMSE 607 : 0.1520294 


s: 608 
logit 608 : -0.3475793 
logit mean 608 : -0.4381105 
logit RMSE 608 : 0.07009142 

boosting 608 : -0.4473049 
boosting mean 608 : -0.482642 
boosting RMSE 608 : 0.1385741 

forest 608 : -0.3540905 
forest mean 608 : -0.3924155 
forest RMSE 608 : 0.05180794 

nnet 608 : -0.1264856 
nnet mean 608 : -0.4187902 
nnet RMSE 608 : 0.1523088 


s: 609 
logit 609 : -0.4872783 
logit mean 609 : -0.4381912 
logit RMSE 609 : 0.07012309 

boosting 609 : -0.3849042 
boosting mean 609 : -0.4824815 
boosting RMSE 609 : 0.1384616 

forest 609 : -0.4296838 
forest mean 609 : -0.3924766 
forest RMSE 609 : 0.05177936 

nnet 609 : -0.4324565 
nnet mean 609 : -0.4188127 
nnet RMSE 609 : 0.1521893 


s: 610 
logit 610 : -0.5683715 
logit mean 610 : -0.4384047 
logit RMSE 610 : 0.07039645 

boosting 610 : -0.4342793 
boosting mean 610 : -0.4824025 
boosting RMSE 610 : 0.1383550 

forest 610 : -0.341895 
forest mean 610 : -0.3923937 
forest RMSE 610 : 0.05179036 

nnet 610 : -0.4307495 
nnet mean 610 : -0.4188323 
nnet RMSE 610 : 0.1520696 


s: 611 
logit 611 : -0.438956 
logit mean 611 : -0.4384056 
logit RMSE 611 : 0.07035647 

boosting 611 : -0.4774954 
boosting mean 611 : -0.4823944 
boosting RMSE 611 : 0.1382773 

forest 611 : -0.410868 
forest mean 611 : -0.392424 
forest RMSE 611 : 0.05174983 

nnet 611 : -0.4823144 
nnet mean 611 : -0.4189362 
nnet RMSE 611 : 0.1519816 


s: 612 
logit 612 : -0.4362672 
logit mean 612 : -0.4384021 
logit RMSE 612 : 0.07031425 

boosting 612 : -0.4119231 
boosting mean 612 : -0.4822793 
boosting RMSE 612 : 0.1381651 

forest 612 : -0.3856108 
forest mean 612 : -0.3924128 
forest RMSE 612 : 0.0517108 

nnet 612 : -0.4601831 
nnet mean 612 : -0.4190035 
nnet RMSE 612 : 0.1518769 


s: 613 
logit 613 : -0.5258594 
logit mean 613 : -0.4385447 
logit RMSE 613 : 0.07044054 

boosting 613 : -0.6050773 
boosting mean 613 : -0.4824796 
boosting RMSE 613 : 0.1383006 

forest 613 : -0.3812795 
forest mean 613 : -0.3923947 
forest RMSE 613 : 0.05167414 

nnet 613 : -0.6083002 
nnet mean 613 : -0.4193124 
nnet RMSE 613 : 0.151986 


s: 614 
logit 614 : -0.4977429 
logit mean 614 : -0.4386411 
logit RMSE 614 : 0.0704936 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 614 : -0.5841779 
boosting mean 614 : -0.4826452 
boosting RMSE 614 : 0.1383877 

forest 614 : -0.421495 
forest mean 614 : -0.3924421 
forest RMSE 614 : 0.05163933 

nnet 614 : -0.3317643 
nnet mean 614 : -0.4191698 
nnet RMSE 614 : 0.1518872 


s: 615 
logit 615 : -0.4124438 
logit mean 615 : -0.4385985 
logit RMSE 615 : 0.07043806 

boosting 615 : -0.5090264 
boosting mean 615 : -0.4826881 
boosting RMSE 615 : 0.1383450 

forest 615 : -0.4774986 
forest mean 615 : -0.3925804 
forest RMSE 615 : 0.05169188 

nnet 615 : -0.4558738 
nnet mean 615 : -0.4192294 
nnet RMSE 615 : 0.1517803 


s: 616 
logit 616 : -0.4168473 
logit mean 616 : -0.4385632 
logit RMSE 616 : 0.07038414 

boosting 616 : -0.5682607 
boosting mean 616 : -0.4828271 
boosting RMSE 616 : 0.1383989 

forest 616 : -0.3550939 
forest mean 616 : -0.3925195 
forest RMSE 616 : 0.05168158 

nnet 616 : -0.3587656 
nnet mean 616 : -0.4191313 
nnet RMSE 616 : 0.1516662 


s: 617 
logit 617 : -0.5221537 
logit mean 617 : -0.4386987 
logit RMSE 617 : 0.0704988 

boosting 617 : -0.4687446 
boosting mean 617 : -0.4828042 
boosting RMSE 617 : 0.1383143 

forest 617 : -0.3334145 
forest mean 617 : -0.3924237 
forest RMSE 617 : 0.05170922 

nnet 617 : -0.3319958 
nnet mean 617 : -0.4189901 
nnet RMSE 617 : 0.1515680 


s: 618 
logit 618 : -0.4200359 
logit mean 618 : -0.4386685 
logit RMSE 618 : 0.07044635 

boosting 618 : -0.4113978 
boosting mean 618 : -0.4826887 
boosting RMSE 618 : 0.1382032 

forest 618 : -0.3707259 
forest mean 618 : -0.3923886 
forest RMSE 618 : 0.05168078 

nnet 618 : -0.2651794 
nnet mean 618 : -0.4187412 
nnet RMSE 618 : 0.1515424 


s: 619 
logit 619 : -0.5369411 
logit mean 619 : -0.4388273 
logit RMSE 619 : 0.0706043 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 619 : -0.3886286 
boosting mean 619 : -0.4825367 
boosting RMSE 619 : 0.1380922 

forest 619 : -0.3533372 
forest mean 619 : -0.3923255 
forest RMSE 619 : 0.05167307 

nnet 619 : -0.2206081 
nnet mean 619 : -0.4184211 
nnet RMSE 619 : 0.1515915 


s: 620 
logit 620 : -0.542887 
logit mean 620 : -0.4389951 
logit RMSE 620 : 0.07078034 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 620 : -0.478603 
boosting mean 620 : -0.4825304 
boosting RMSE 620 : 0.1380169 

forest 620 : -0.5340489 
forest mean 620 : -0.3925541 
forest RMSE 620 : 0.05191129 

nnet 620 : -0.1171760 
nnet mean 620 : -0.4179352 
nnet RMSE 620 : 0.1518945 


s: 621 
logit 621 : -0.3633623 
logit mean 621 : -0.4388733 
logit RMSE 621 : 0.07073861 

boosting 621 : -0.5051942 
boosting mean 621 : -0.4825669 
boosting RMSE 621 : 0.1379704 

forest 621 : -0.3569948 
forest mean 621 : -0.3924968 
forest RMSE 621 : 0.05189817 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 621 : -0.2258648 
nnet mean 621 : -0.4176259 
nnet RMSE 621 : 0.1519329 


s: 622 
logit 622 : -0.4309585 
logit mean 622 : -0.4388606 
logit RMSE 622 : 0.07069262 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 622 : -0.4760774 
boosting mean 622 : -0.4825565 
boosting RMSE 622 : 0.1378931 

forest 622 : -0.4051908 
forest mean 622 : -0.3925173 
forest RMSE 622 : 0.05185685 

nnet 622 : -0.5298861 
nnet mean 622 : -0.4178064 
nnet RMSE 622 : 0.1519000 


s: 623 
logit 623 : -0.3379190 
logit mean 623 : -0.4386986 
logit RMSE 623 : 0.07067964 

boosting 623 : -0.4009751 
boosting mean 623 : -0.4824255 
boosting RMSE 623 : 0.1377824 

forest 623 : -0.3606681 
forest mean 623 : -0.3924661 
forest RMSE 623 : 0.05183918 

nnet 623 : -0.3166955 
nnet mean 623 : -0.4176441 
nnet RMSE 623 : 0.1518148 


s: 624 
logit 624 : -0.3777058 
logit mean 624 : -0.4386008 
logit RMSE 624 : 0.07062862 

boosting 624 : -0.4401886 
boosting mean 624 : -0.4823578 
boosting RMSE 624 : 0.1376814 

forest 624 : -0.3814815 
forest mean 624 : -0.3924485 
forest RMSE 624 : 0.05180293 

nnet 624 : -0.5050884 
nnet mean 624 : -0.4177842 
nnet RMSE 624 : 0.1517514 


s: 625 
logit 625 : -0.4826228 
logit mean 625 : -0.4386713 
logit RMSE 625 : 0.07064944 

boosting 625 : -0.5594477 
boosting mean 625 : -0.4824812 
boosting RMSE 625 : 0.1377190 

forest 625 : -0.4002517 
forest mean 625 : -0.392461 
forest RMSE 625 : 0.05176147 

nnet 625 : -0.2332058 
nnet mean 625 : -0.4174889 
nnet RMSE 625 : 0.1517766 


s: 626 
logit 626 : -0.4158607 
logit mean 626 : -0.4386348 
logit RMSE 626 : 0.07059583 

boosting 626 : -0.5223445 
boosting mean 626 : -0.4825448 
boosting RMSE 626 : 0.1376958 

forest 626 : -0.341759 
forest mean 626 : -0.39238 
forest RMSE 626 : 0.05177247 

nnet 626 : -0.2114953 
nnet mean 626 : -0.4171599 
nnet RMSE 626 : 0.1518424 


s: 627 
logit 627 : -0.4361108 
logit mean 627 : -0.4386308 
logit RMSE 627 : 0.07055426 

boosting 627 : -0.544609 
boosting mean 627 : -0.4826438 
boosting RMSE 627 : 0.1377071 

forest 627 : -0.3968827 
forest mean 627 : -0.3923872 
forest RMSE 627 : 0.05173131 

nnet 627 : -0.4849678 
nnet mean 627 : -0.417268 
nnet RMSE 627 : 0.1517592 


s: 628 
logit 628 : -0.4422418 
logit mean 628 : -0.4386366 
logit RMSE 628 : 0.07051821 

boosting 628 : -0.4502948 
boosting mean 628 : -0.4825923 
boosting RMSE 628 : 0.1376120 

forest 628 : -0.350962 
forest mean 628 : -0.3923212 
forest RMSE 628 : 0.05172714 

nnet 628 : -0.504314 
nnet mean 628 : -0.4174066 
nnet RMSE 628 : 0.1516954 


s: 629 
logit 629 : -0.4285982 
logit mean 629 : -0.4386206 
logit RMSE 629 : 0.07047136 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 629 : -0.4677072 
boosting mean 629 : -0.4825686 
boosting RMSE 629 : 0.1375291 

forest 629 : -0.4124731 
forest mean 629 : -0.3923533 
forest RMSE 629 : 0.05168839 

nnet 629 : -0.2475709 
nnet mean 629 : -0.4171366 
nnet RMSE 629 : 0.1516966 


s: 630 
logit 630 : -0.467935 
logit mean 630 : -0.4386671 
logit RMSE 630 : 0.0704674 

boosting 630 : -0.5195156 
boosting mean 630 : -0.4826273 
boosting RMSE 630 : 0.1375024 

forest 630 : -0.3135198 
forest mean 630 : -0.3922281 
forest RMSE 630 : 0.05176215 

nnet 630 : -0.8277972 
nnet mean 630 : -0.4177884 
nnet RMSE 630 : 0.1525314 


s: 631 
logit 631 : -0.4038075 
logit mean 631 : -0.4386119 
logit RMSE 631 : 0.0704117 

boosting 631 : -0.4319009 
boosting mean 631 : -0.4825469 
boosting RMSE 631 : 0.1373992 

forest 631 : -0.4053105 
forest mean 631 : -0.3922489 
forest RMSE 631 : 0.05172155 

nnet 631 : -0.1269819 
nnet mean 631 : -0.4173276 
nnet RMSE 631 : 0.1527975 


s: 632 
logit 632 : -0.4951031 
logit mean 632 : -0.4387013 
logit RMSE 632 : 0.07045761 

boosting 632 : -0.5163989 
boosting mean 632 : -0.4826005 
boosting RMSE 632 : 0.1373685 

forest 632 : -0.3968619 
forest mean 632 : -0.3922562 
forest RMSE 632 : 0.05168077 

nnet 632 : -0.3579301 
nnet mean 632 : -0.4172336 
nnet RMSE 632 : 0.1526858 


s: 633 
logit 633 : -0.5128047 
logit mean 633 : -0.4388183 
logit RMSE 633 : 0.07054456 

boosting 633 : -0.4932711 
boosting mean 633 : -0.4826173 
boosting RMSE 633 : 0.1373101 

forest 633 : -0.4052156 
forest mean 633 : -0.3922766 
forest RMSE 633 : 0.05164035 

nnet 633 : -0.6059437 
nnet mean 633 : -0.4175317 
nnet RMSE 633 : 0.1527846 


s: 634 
logit 634 : -0.3816809 
logit mean 634 : -0.4387282 
logit RMSE 634 : 0.07049266 

boosting 634 : -0.5474003 
boosting mean 634 : -0.4827195 
boosting RMSE 634 : 0.1373266 

forest 634 : -0.4208059 
forest mean 634 : -0.3923216 
forest RMSE 634 : 0.05160622 

nnet 634 : -0.5270247 
nnet mean 634 : -0.4177044 
nnet RMSE 634 : 0.1527473 


s: 635 
logit 635 : -0.4282390 
logit mean 635 : -0.4387117 
logit RMSE 635 : 0.07044604 

boosting 635 : -0.5177916 
boosting mean 635 : -0.4827747 
boosting RMSE 635 : 0.1372980 

forest 635 : -0.3182411 
forest mean 635 : -0.392205 
forest RMSE 635 : 0.05166754 

nnet 635 : -0.3471165 
nnet mean 635 : -0.4175933 
nnet RMSE 635 : 0.1526414 


s: 636 
logit 636 : -0.3991989 
logit mean 636 : -0.4386496 
logit RMSE 636 : 0.07039065 

boosting 636 : -0.2534552 
boosting mean 636 : -0.4824142 
boosting RMSE 636 : 0.137313 

forest 636 : -0.3871947 
forest mean 636 : -0.3921971 
forest RMSE 636 : 0.0516294 

nnet 636 : -0.5402766 
nnet mean 636 : -0.4177862 
nnet RMSE 636 : 0.1526228 


s: 637 
logit 637 : -0.5430802 
logit mean 637 : -0.4388135 
logit RMSE 637 : 0.07056347 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 637 : -0.4842417 
boosting mean 637 : -0.482417 
boosting RMSE 637 : 0.1372458 

forest 637 : -0.3959978 
forest mean 637 : -0.3922031 
forest RMSE 637 : 0.0515891 

nnet 637 : -0.4868678 
nnet mean 637 : -0.4178946 
nnet RMSE 637 : 0.1525418 


s: 638 
logit 638 : -0.4846004 
logit mean 638 : -0.4388853 
logit RMSE 638 : 0.07058765 

boosting 638 : -0.4586517 
boosting mean 638 : -0.4823798 
boosting RMSE 638 : 0.1371578 

forest 638 : -0.4056904 
forest mean 638 : -0.3922242 
forest RMSE 638 : 0.05154915 

nnet 638 : -0.3557122 
nnet mean 638 : -0.4177971 
nnet RMSE 638 : 0.1524323 


s: 639 
logit 639 : -0.4821845 
logit mean 639 : -0.438953 
logit RMSE 639 : 0.07060729 

boosting 639 : -0.6604052 
boosting mean 639 : -0.4826584 
boosting RMSE 639 : 0.1374371 

forest 639 : -0.4083575 
forest mean 639 : -0.3922495 
forest RMSE 639 : 0.05150986 

nnet 639 : -0.4852301 
nnet mean 639 : -0.4179027 
nnet RMSE 639 : 0.1523503 


s: 640 
logit 640 : -0.3825918 
logit mean 640 : -0.438865 
logit RMSE 640 : 0.07055546 

boosting 640 : -0.2983315 
boosting mean 640 : -0.4823704 
boosting RMSE 640 : 0.1373884 

forest 640 : -0.2835298 
forest mean 640 : -0.3920796 
forest RMSE 640 : 0.0516751 

nnet 640 : -0.30013 
nnet mean 640 : -0.4177186 
nnet RMSE 640 : 0.1522824 


s: 641 
logit 641 : -0.5752396 
logit mean 641 : -0.4390777 
logit RMSE 641 : 0.07083936 

boosting 641 : -0.4403876 
boosting mean 641 : -0.4823049 
boosting RMSE 641 : 0.1372905 

forest 641 : -0.4233166 
forest mean 641 : -0.3921283 
forest RMSE 641 : 0.05164299 

nnet 641 : -0.3174094 
nnet mean 641 : -0.4175622 
nnet RMSE 641 : 0.1521985 


s: 642 
logit 642 : -0.4424889 
logit mean 642 : -0.439083 
logit RMSE 642 : 0.07080403 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 642 : -0.4407708 
boosting mean 642 : -0.4822402 
boosting RMSE 642 : 0.1371930 

forest 642 : -0.364501 
forest mean 642 : -0.3920853 
forest RMSE 642 : 0.05162177 

nnet 642 : -0.376226 
nnet mean 642 : -0.4174978 
nnet RMSE 642 : 0.1520828 


s: 643 
logit 643 : -0.4485238 
logit mean 643 : -0.4390977 
logit RMSE 643 : 0.07077482 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 643 : -0.5386871 
boosting mean 643 : -0.482328 
boosting RMSE 643 : 0.1371953 

forest 643 : -0.4125213 
forest mean 643 : -0.3921171 
forest RMSE 643 : 0.05158397 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 643 : -0.2260606 
nnet mean 643 : -0.4172000 
nnet RMSE 643 : 0.1521192 


s: 644 
logit 644 : -0.4165566 
logit mean 644 : -0.4390627 
logit RMSE 644 : 0.07072286 

boosting 644 : -0.4874285 
boosting mean 644 : -0.4823359 
boosting RMSE 644 : 0.1371320 

forest 644 : -0.4075525 
forest mean 644 : -0.392141 
forest RMSE 644 : 0.05154477 

nnet 644 : -0.4263091 
nnet mean 644 : -0.4172142 
nnet RMSE 644 : 0.1520046 


s: 645 
logit 645 : -0.5139601 
logit mean 645 : -0.4391788 
logit RMSE 645 : 0.07081033 

boosting 645 : -0.5124797 
boosting mean 645 : -0.4823826 
boosting RMSE 645 : 0.1370972 

forest 645 : -0.4431075 
forest mean 645 : -0.3922201 
forest RMSE 645 : 0.05153275 

nnet 645 : -0.2943841 
nnet mean 645 : -0.4170238 
nnet RMSE 645 : 0.1519437 


s: 646 
logit 646 : -0.4532725 
logit mean 646 : -0.4392006 
logit RMSE 646 : 0.07078654 

boosting 646 : -0.5191488 
boosting mean 646 : -0.4824395 
boosting RMSE 646 : 0.1370713 

forest 646 : -0.383941 
forest mean 646 : -0.3922072 
forest RMSE 646 : 0.05149673 

nnet 646 : -0.3382849 
nnet mean 646 : -0.4169019 
nnet RMSE 646 : 0.1518454 


s: 647 
logit 647 : -0.6378854 
logit mean 647 : -0.4395077 
logit RMSE 647 : 0.07134742 

boosting 647 : -0.5750275 
boosting mean 647 : -0.4825826 
boosting RMSE 647 : 0.1371381 

forest 647 : -0.5085847 
forest mean 647 : -0.3923871 
forest RMSE 647 : 0.05163369 

nnet 647 : -0.5419304 
nnet mean 647 : -0.4170951 
nnet RMSE 647 : 0.1518306 


s: 648 
logit 648 : -0.4363606 
logit mean 648 : -0.4395029 
logit RMSE 648 : 0.07130665 

boosting 648 : -0.4098209 
boosting mean 648 : -0.4824704 
boosting RMSE 648 : 0.1370327 

forest 648 : -0.3951434 
forest mean 648 : -0.3923914 
forest RMSE 648 : 0.05159419 

nnet 648 : -0.41242 
nnet mean 648 : -0.4170879 
nnet RMSE 648 : 0.1517142 


s: 649 
logit 649 : -0.4717212 
logit mean 649 : -0.4395525 
logit RMSE 649 : 0.0713073 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 649 : -0.4705198 
boosting mean 649 : -0.4824519 
boosting RMSE 649 : 0.1369551 

forest 649 : -0.4553447 
forest mean 649 : -0.3924884 
forest RMSE 649 : 0.05160017 

nnet 649 : -0.5169961 
nnet mean 649 : -0.4172418 
nnet RMSE 649 : 0.1516668 


s: 650 
logit 650 : -0.4916854 
logit mean 650 : -0.4396327 
logit RMSE 650 : 0.07134312 

boosting 650 : -0.4912052 
boosting mean 650 : -0.4824654 
boosting RMSE 650 : 0.1368965 

forest 650 : -0.4471858 
forest mean 650 : -0.3925725 
forest RMSE 650 : 0.05159367 

nnet 650 : -0.2761148 
nnet mean 650 : -0.4170247 
nnet RMSE 650 : 0.151628 


s: 651 
logit 651 : -0.4890525 
logit mean 651 : -0.4397086 
logit RMSE 651 : 0.07137369 

boosting 651 : -0.6688949 
boosting mean 651 : -0.4827518 
boosting RMSE 651 : 0.1371966 

forest 651 : -0.3986962 
forest mean 651 : -0.3925819 
forest RMSE 651 : 0.05155406 

nnet 651 : -0.4507905 
nnet mean 651 : -0.4170766 
nnet RMSE 651 : 0.1515246 


s: 652 
logit 652 : -0.451684 
logit mean 652 : -0.439727 
logit RMSE 652 : 0.07134765 

boosting 652 : -0.5739894 
boosting mean 652 : -0.4828917 
boosting RMSE 652 : 0.1372606 

forest 652 : -0.3848814 
forest mean 652 : -0.3925701 
forest RMSE 652 : 0.05151791 

nnet 652 : -0.3575726 
nnet mean 652 : -0.4169853 
nnet RMSE 652 : 0.1514174 


s: 653 
logit 653 : -0.3753516 
logit mean 653 : -0.4396284 
logit RMSE 653 : 0.07129952 

boosting 653 : -0.5234637 
boosting mean 653 : -0.4829539 
boosting RMSE 653 : 0.1372406 

forest 653 : -0.4367067 
forest mean 653 : -0.3926377 
forest RMSE 653 : 0.05149848 

nnet 653 : -0.2911252 
nnet mean 653 : -0.4167926 
nnet RMSE 653 : 0.1513614 


s: 654 
logit 654 : -0.3932135 
logit mean 654 : -0.4395575 
logit RMSE 654 : 0.07124549 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 654 : -0.3691026 
boosting mean 654 : -0.4827798 
boosting RMSE 654 : 0.1371409 

forest 654 : -0.4655022 
forest mean 654 : -0.3927491 
forest RMSE 654 : 0.0515228 

nnet 654 : -0.3009003 
nnet mean 654 : -0.4166154 
nnet RMSE 654 : 0.1512953 


s: 655 
logit 655 : -0.422746 
logit mean 655 : -0.4395318 
logit RMSE 655 : 0.07119663 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 655 : -0.2073392 
boosting mean 655 : -0.4823593 
boosting RMSE 655 : 0.1372428 

forest 655 : -0.3173395 
forest mean 655 : -0.392634 
forest RMSE 655 : 0.05158467 

nnet 655 : -0.1679341 
nnet mean 655 : -0.4162357 
nnet RMSE 655 : 0.1514514 


s: 656 
logit 656 : -0.3933909 
logit mean 656 : -0.4394614 
logit RMSE 656 : 0.07114281 

boosting 656 : -0.4078986 
boosting mean 656 : -0.4822457 
boosting RMSE 656 : 0.1371385 

forest 656 : -0.3467508 
forest mean 656 : -0.3925640 
forest RMSE 656 : 0.05158725 

nnet 656 : -0.2277385 
nnet mean 656 : -0.4159484 
nnet RMSE 656 : 0.1514853 


s: 657 
logit 657 : -0.4608818 
logit mean 657 : -0.4394941 
logit RMSE 657 : 0.07112832 

boosting 657 : -0.4958516 
boosting mean 657 : -0.4822665 
boosting RMSE 657 : 0.1370851 

forest 657 : -0.3117029 
forest mean 657 : -0.392441 
forest RMSE 657 : 0.05166295 

nnet 657 : -0.3449623 
nnet mean 657 : -0.4158403 
nnet RMSE 657 : 0.1513852 


s: 658 
logit 658 : -0.417306 
logit mean 658 : -0.4394603 
logit RMSE 658 : 0.07107745 

boosting 658 : -0.6143716 
boosting mean 658 : -0.4824672 
boosting RMSE 658 : 0.1372356 

forest 658 : -0.3637326 
forest mean 658 : -0.3923973 
forest RMSE 658 : 0.05164303 

nnet 658 : -0.5668576 
nnet mean 658 : -0.4160698 
nnet RMSE 658 : 0.1514100 


s: 659 
logit 659 : -0.4801838 
logit mean 659 : -0.4395221 
logit RMSE 659 : 0.07109215 

boosting 659 : -0.4279228 
boosting mean 659 : -0.4823845 
boosting RMSE 659 : 0.1371357 

forest 659 : -0.3102942 
forest mean 659 : -0.3922727 
forest RMSE 659 : 0.05172201 

nnet 659 : -0.3860802 
nnet mean 659 : -0.4160243 
nnet RMSE 659 : 0.151296 


s: 660 
logit 660 : -0.3386739 
logit mean 660 : -0.4393693 
logit RMSE 660 : 0.07107837 

boosting 660 : -0.4153851 
boosting mean 660 : -0.4822829 
boosting RMSE 660 : 0.1370331 

forest 660 : -0.3534055 
forest mean 660 : -0.3922139 
forest RMSE 660 : 0.05171463 

nnet 660 : -0.3996614 
nnet mean 660 : -0.4159995 
nnet RMSE 660 : 0.1511813 


s: 661 
logit 661 : -0.339061 
logit mean 661 : -0.4392176 
logit RMSE 661 : 0.07106412 

boosting 661 : -0.3478268 
boosting mean 661 : -0.4820795 
boosting RMSE 661 : 0.1369445 

forest 661 : -0.3994587 
forest mean 661 : -0.3922248 
forest RMSE 661 : 0.0516755 

nnet 661 : -0.6667358 
nnet mean 661 : -0.4163789 
nnet RMSE 661 : 0.1514228 


s: 662 
logit 662 : -0.4997328 
logit mean 662 : -0.439309 
logit RMSE 662 : 0.07111615 

boosting 662 : -0.3922721 
boosting mean 662 : -0.4819439 
boosting RMSE 662 : 0.1368413 

forest 662 : -0.4633953 
forest mean 662 : -0.3923323 
forest RMSE 662 : 0.05169521 

nnet 662 : -0.2641058 
nnet mean 662 : -0.4161488 
nnet RMSE 662 : 0.1514005 


s: 663 
logit 663 : -0.4349324 
logit mean 663 : -0.4393024 
logit RMSE 663 : 0.07107544 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 663 : -0.4320706 
boosting mean 663 : -0.4818686 
boosting RMSE 663 : 0.1367438 

forest 663 : -0.3691909 
forest mean 663 : -0.3922974 
forest RMSE 663 : 0.05167006 

nnet 663 : -0.2022792 
nnet mean 663 : -0.4158263 
nnet RMSE 663 : 0.1514811 


s: 664 
logit 664 : -0.3894841 
logit mean 664 : -0.4392274 
logit RMSE 664 : 0.07102307 

boosting 664 : -0.2536958 
boosting mean 664 : -0.481525 
boosting RMSE 664 : 0.1367587 

forest 664 : -0.3207785 
forest mean 664 : -0.3921897 
forest RMSE 664 : 0.05172259 

nnet 664 : -0.3530146 
nnet mean 664 : -0.4157317 
nnet RMSE 664 : 0.1513779 


s: 665 
logit 665 : -0.3811708 
logit mean 665 : -0.4391401 
logit RMSE 665 : 0.07097341 

boosting 665 : -0.3956383 
boosting mean 665 : -0.4813959 
boosting RMSE 665 : 0.1366559 

forest 665 : -0.2742175 
forest mean 665 : -0.3920123 
forest RMSE 665 : 0.05191334 

nnet 665 : -0.05690701 
nnet mean 665 : -0.4151921 
nnet RMSE 665 : 0.1518480 


s: 666 
logit 666 : -0.4465035 
logit mean 666 : -0.4391511 
logit RMSE 666 : 0.070943 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 666 : -0.4469833 
boosting mean 666 : -0.4813442 
boosting RMSE 666 : 0.1365654 

forest 666 : -0.3426091 
forest mean 666 : -0.3919381 
forest RMSE 666 : 0.051922 

nnet 666 : -0.3127957 
nnet mean 666 : -0.4150383 
nnet RMSE 666 : 0.1517716 


s: 667 
logit 667 : -0.4157694 
logit mean 667 : -0.4391161 
logit RMSE 667 : 0.07089242 

boosting 667 : -0.4229601 
boosting mean 667 : -0.4812567 
boosting RMSE 667 : 0.1364659 

forest 667 : -0.3842962 
forest mean 667 : -0.3919267 
forest RMSE 667 : 0.05188662 

nnet 667 : -0.408821 
nnet mean 667 : -0.415029 
nnet RMSE 667 : 0.1516582 


s: 668 
logit 668 : -0.3738752 
logit mean 668 : -0.4390184 
logit RMSE 668 : 0.07084655 

boosting 668 : -0.4755124 
boosting mean 668 : -0.4812481 
boosting RMSE 668 : 0.136395 

forest 668 : -0.4410539 
forest mean 668 : -0.3920002 
forest RMSE 668 : 0.0518721 

nnet 668 : -0.3449960 
nnet mean 668 : -0.4149242 
nnet RMSE 668 : 0.1515596 


s: 669 
logit 669 : -0.3544577 
logit mean 669 : -0.438892 
logit RMSE 669 : 0.07081548 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 669 : -0.5644474 
boosting mean 669 : -0.4813724 
boosting RMSE 669 : 0.1364412 

forest 669 : -0.4222324 
forest mean 669 : -0.3920454 
forest RMSE 669 : 0.05184044 

nnet 669 : -0.457934 
nnet mean 669 : -0.4149885 
nnet RMSE 669 : 0.1514628 


s: 670 
logit 670 : -0.3132073 
logit mean 670 : -0.4387044 
logit RMSE 670 : 0.070842 

boosting 670 : -0.5375794 
boosting mean 670 : -0.4814563 
boosting RMSE 670 : 0.1364429 

forest 670 : -0.431419 
forest mean 670 : -0.3921042 
forest RMSE 670 : 0.05181596 

nnet 670 : -0.5940916 
nnet mean 670 : -0.4152558 
nnet RMSE 670 : 0.1515354 


s: 671 
logit 671 : -0.4255671 
logit mean 671 : -0.4386848 
logit RMSE 671 : 0.07079608 

boosting 671 : -0.5001971 
boosting mean 671 : -0.4814842 
boosting RMSE 671 : 0.1363961 

forest 671 : -0.3427351 
forest mean 671 : -0.3920306 
forest RMSE 671 : 0.05182451 

nnet 671 : -0.6810188 
nnet mean 671 : -0.4156519 
nnet RMSE 671 : 0.1518106 


s: 672 
logit 672 : -0.4517867 
logit mean 672 : -0.4387043 
logit RMSE 672 : 0.07077159 

boosting 672 : -0.4440865 
boosting mean 672 : -0.4814286 
boosting RMSE 672 : 0.1363052 

forest 672 : -0.3646323 
forest mean 672 : -0.3919898 
forest RMSE 672 : 0.0518039 

nnet 672 : -0.05857106 
nnet mean 672 : -0.4151205 
nnet RMSE 672 : 0.1522683 


s: 673 
logit 673 : -0.3917601 
logit mean 673 : -0.4386346 
logit RMSE 673 : 0.0707197 

boosting 673 : -0.4008176 
boosting mean 673 : -0.4813088 
boosting RMSE 673 : 0.1362039 

forest 673 : -0.3702600 
forest mean 673 : -0.3919575 
forest RMSE 673 : 0.05177809 

nnet 673 : -0.1544827 
nnet mean 673 : -0.4147332 
nnet RMSE 673 : 0.1524491 


s: 674 
logit 674 : -0.4835772 
logit mean 674 : -0.4387012 
logit RMSE 674 : 0.07074051 

boosting 674 : -0.3321933 
boosting mean 674 : -0.4810876 
boosting RMSE 674 : 0.1361279 

forest 674 : -0.4198960 
forest mean 674 : -0.391999 
forest RMSE 674 : 0.05174534 

nnet 674 : -0.3990672 
nnet mean 674 : -0.41471 
nnet RMSE 674 : 0.152336 


s: 675 
logit 675 : -0.3315073 
logit mean 675 : -0.4385424 
logit RMSE 675 : 0.07073723 

boosting 675 : -0.5963013 
boosting mean 675 : -0.4812583 
boosting RMSE 675 : 0.1362367 

forest 675 : -0.4285503 
forest mean 675 : -0.3920531 
forest RMSE 675 : 0.05171867 

nnet 675 : -0.491582 
nnet mean 675 : -0.4148238 
nnet RMSE 675 : 0.1522639 


s: 676 
logit 676 : -0.4886224 
logit mean 676 : -0.4386165 
logit RMSE 676 : 0.07076703 

boosting 676 : -0.450049 
boosting mean 676 : -0.4812121 
boosting RMSE 676 : 0.1361495 

forest 676 : -0.4166426 
forest mean 676 : -0.3920895 
forest RMSE 676 : 0.05168437 

nnet 676 : -0.3296651 
nnet mean 676 : -0.4146979 
nnet RMSE 676 : 0.1521753 


s: 677 
logit 677 : -0.4871659 
logit mean 677 : -0.4386882 
logit RMSE 677 : 0.07079405 

boosting 677 : -0.1177182 
boosting mean 677 : -0.4806752 
boosting RMSE 677 : 0.1364808 

forest 677 : -0.413144 
forest mean 677 : -0.3921206 
forest RMSE 677 : 0.05164866 

nnet 677 : -0.3067234 
nnet mean 677 : -0.4145384 
nnet RMSE 677 : 0.1521051 


s: 678 
logit 678 : -0.3844333 
logit mean 678 : -0.4386082 
logit RMSE 678 : 0.07074435 

boosting 678 : -0.5184083 
boosting mean 678 : -0.4807308 
boosting RMSE 678 : 0.1364559 

forest 678 : -0.3548347 
forest mean 678 : -0.3920656 
forest RMSE 678 : 0.05163969 

nnet 678 : -0.6171945 
nnet mean 678 : -0.4148373 
nnet RMSE 678 : 0.1522216 


s: 679 
logit 679 : -0.4069326 
logit mean 679 : -0.4385616 
logit RMSE 679 : 0.07069274 

boosting 679 : -0.4747012 
boosting mean 679 : -0.4807219 
boosting RMSE 679 : 0.1363855 

forest 679 : -0.3905846 
forest mean 679 : -0.3920634 
forest RMSE 679 : 0.05160292 

nnet 679 : -0.1684008 
nnet mean 679 : -0.4144743 
nnet RMSE 679 : 0.1523689 


s: 680 
logit 680 : -0.4511604 
logit mean 680 : -0.4385801 
logit RMSE 680 : 0.07066798 

boosting 680 : -0.4730853 
boosting mean 680 : -0.4807107 
boosting RMSE 680 : 0.1363140 

forest 680 : -0.2872941 
forest mean 680 : -0.3919094 
forest RMSE 680 : 0.05174578 

nnet 680 : -0.432373 
nnet mean 680 : -0.4145007 
nnet RMSE 680 : 0.1522619 


s: 681 
logit 681 : -0.5528194 
logit mean 681 : -0.4387478 
logit RMSE 681 : 0.07085847 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 681 : -0.5555936 
boosting mean 681 : -0.4808207 
boosting RMSE 681 : 0.1363443 

forest 681 : -0.4198797 
forest mean 681 : -0.3919504 
forest RMSE 681 : 0.05171338 

nnet 681 : -0.687725 
nnet mean 681 : -0.4149019 
nnet RMSE 681 : 0.1525491 


s: 682 
logit 682 : -0.2914253 
logit mean 682 : -0.4385318 
logit RMSE 682 : 0.07092846 

boosting 682 : -0.4731788 
boosting mean 682 : -0.4808095 
boosting RMSE 682 : 0.1362731 

forest 682 : -0.3516893 
forest mean 682 : -0.3918914 
forest RMSE 682 : 0.05170856 

nnet 682 : -0.4666151 
nnet mean 682 : -0.4149777 
nnet RMSE 682 : 0.1524585 


s: 683 
logit 683 : -0.4591349 
logit mean 683 : -0.438562 
logit RMSE 683 : 0.07091262 

boosting 683 : -0.4413246 
boosting mean 683 : -0.4807517 
boosting RMSE 683 : 0.1361825 

forest 683 : -0.3664153 
forest mean 683 : -0.3918541 
forest RMSE 683 : 0.05168667 

nnet 683 : -0.4583953 
nnet mean 683 : -0.4150413 
nnet RMSE 683 : 0.1523633 


s: 684 
logit 684 : -0.5155504 
logit mean 684 : -0.4386746 
logit RMSE 684 : 0.07099837 

boosting 684 : -0.5633307 
boosting mean 684 : -0.4808724 
boosting RMSE 684 : 0.1362261 

forest 684 : -0.3936963 
forest mean 684 : -0.3918568 
forest RMSE 684 : 0.05164943 

nnet 684 : -0.7382633 
nnet mean 684 : -0.4155138 
nnet RMSE 684 : 0.1528002 


s: 685 
logit 685 : -0.447777 
logit mean 685 : -0.4386878 
logit RMSE 685 : 0.07097001 

boosting 685 : -0.5991433 
boosting mean 685 : -0.481045 
boosting RMSE 685 : 0.1363391 

forest 685 : -0.3063957 
forest mean 685 : -0.391732 
forest RMSE 685 : 0.05173548 

nnet 685 : -0.3384057 
nnet mean 685 : -0.4154012 
nnet RMSE 685 : 0.1527068 


s: 686 
logit 686 : -0.3820297 
logit mean 686 : -0.4386052 
logit RMSE 686 : 0.07092158 

boosting 686 : -0.4204803 
boosting mean 686 : -0.4809568 
boosting RMSE 686 : 0.1362420 

forest 686 : -0.4165671 
forest mean 686 : -0.3917682 
forest RMSE 686 : 0.05170163 

nnet 686 : -0.466951 
nnet mean 686 : -0.4154764 
nnet RMSE 686 : 0.1526168 


s: 687 
logit 687 : -0.4296874 
logit mean 687 : -0.4385923 
logit RMSE 687 : 0.070879 

boosting 687 : -0.4510797 
boosting mean 687 : -0.4809133 
boosting RMSE 687 : 0.1361567 

forest 687 : -0.40746 
forest mean 687 : -0.3917911 
forest RMSE 687 : 0.05166477 

nnet 687 : -0.4655304 
nnet mean 687 : -0.4155493 
nnet RMSE 687 : 0.1525262 


s: 688 
logit 688 : -0.5285007 
logit mean 688 : -0.4387229 
logit RMSE 688 : 0.0709967 

boosting 688 : -0.6492213 
boosting mean 688 : -0.4811579 
boosting RMSE 688 : 0.1363891 

forest 688 : -0.5404753 
forest mean 688 : -0.3920072 
forest RMSE 688 : 0.05190425 

nnet 688 : -0.5463169 
nnet mean 688 : -0.4157393 
nnet RMSE 688 : 0.1525174 


s: 689 
logit 689 : -0.3980735 
logit mean 689 : -0.4386640 
logit RMSE 689 : 0.07094519 

boosting 689 : -0.6706206 
boosting mean 689 : -0.4814329 
boosting RMSE 689 : 0.1366795 

forest 689 : -0.4160676 
forest mean 689 : -0.3920421 
forest RMSE 689 : 0.05187018 

nnet 689 : -0.508558 
nnet mean 689 : -0.415874 
nnet RMSE 689 : 0.1524628 


s: 690 
logit 690 : -0.3723724 
logit mean 690 : -0.4385679 
logit RMSE 690 : 0.07090157 

boosting 690 : -0.2781521 
boosting mean 690 : -0.4811383 
boosting RMSE 690 : 0.1366591 

forest 690 : -0.4077781 
forest mean 690 : -0.3920649 
forest RMSE 690 : 0.05183343 

nnet 690 : -0.275805 
nnet mean 690 : -0.415671 
nnet RMSE 690 : 0.1524256 


s: 691 
logit 691 : -0.3943484 
logit mean 691 : -0.4385039 
logit RMSE 691 : 0.07085057 

boosting 691 : -0.5015144 
boosting mean 691 : -0.4811678 
boosting RMSE 691 : 0.1366148 

forest 691 : -0.3788183 
forest mean 691 : -0.3920457 
forest RMSE 691 : 0.05180218 

nnet 691 : -0.4734453 
nnet mean 691 : -0.4157546 
nnet RMSE 691 : 0.1523409 


s: 692 
logit 692 : -0.3615881 
logit mean 692 : -0.4383927 
logit RMSE 692 : 0.07081442 

boosting 692 : -0.6741579 
boosting mean 692 : -0.4814466 
boosting RMSE 692 : 0.1369133 

forest 692 : -0.4648905 
forest mean 692 : -0.392151 
forest RMSE 692 : 0.05182347 

nnet 692 : -0.642558 
nnet mean 692 : -0.4160824 
nnet RMSE 692 : 0.1525098 


s: 693 
logit 693 : -0.4080466 
logit mean 693 : -0.4383489 
logit RMSE 693 : 0.07076396 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 693 : -0.3828184 
boosting mean 693 : -0.4813043 
boosting RMSE 693 : 0.1368160 

forest 693 : -0.328632 
forest mean 693 : -0.3920594 
forest RMSE 693 : 0.05185698 

nnet 693 : -0.3527338 
nnet mean 693 : -0.415991 
nnet RMSE 693 : 0.1524103 


s: 694 
logit 694 : -0.4633349 
logit mean 694 : -0.4383849 
logit RMSE 694 : 0.07075382 

boosting 694 : -0.522077 
boosting mean 694 : -0.4813631 
boosting RMSE 694 : 0.1367960 

forest 694 : -0.3798098 
forest mean 694 : -0.3920417 
forest RMSE 694 : 0.05182528 

nnet 694 : -0.5287552 
nnet mean 694 : -0.4161535 
nnet RMSE 694 : 0.1523788 


s: 695 
logit 695 : -0.4153477 
logit mean 695 : -0.4383518 
logit RMSE 695 : 0.0707053 

boosting 695 : -0.3859639 
boosting mean 695 : -0.4812258 
boosting RMSE 695 : 0.1366985 

forest 695 : -0.3326188 
forest mean 695 : -0.3919562 
forest RMSE 695 : 0.05185101 

nnet 695 : -0.4567299 
nnet mean 695 : -0.4162119 
nnet RMSE 695 : 0.1522844 


s: 696 
logit 696 : -0.4484687 
logit mean 696 : -0.4383663 
logit RMSE 696 : 0.07067837 

boosting 696 : -0.589204 
boosting mean 696 : -0.481381 
boosting RMSE 696 : 0.1367884 

forest 696 : -0.4284935 
forest mean 696 : -0.3920087 
forest RMSE 696 : 0.05182501 

nnet 696 : -0.5042463 
nnet mean 696 : -0.4163383 
nnet RMSE 696 : 0.1522262 


s: 697 
logit 697 : -0.4187422 
logit mean 697 : -0.4383382 
logit RMSE 697 : 0.07063121 

boosting 697 : -0.4639674 
boosting mean 697 : -0.481356 
boosting RMSE 697 : 0.1367117 

forest 697 : -0.3793322 
forest mean 697 : -0.3919905 
forest RMSE 697 : 0.05179373 

nnet 697 : -0.3214777 
nnet mean 697 : -0.4162022 
nnet RMSE 697 : 0.1521460 


s: 698 
logit 698 : -0.3814715 
logit mean 698 : -0.4382567 
logit RMSE 698 : 0.07058409 

boosting 698 : -0.4937613 
boosting mean 698 : -0.4813737 
boosting RMSE 698 : 0.1366599 

forest 698 : -0.4474519 
forest mean 698 : -0.39207 
forest RMSE 698 : 0.05178777 

nnet 698 : -0.306019 
nnet mean 698 : -0.4160444 
nnet RMSE 698 : 0.1520786 


s: 699 
logit 699 : -0.4707252 
logit mean 699 : -0.4383032 
logit RMSE 699 : 0.07058429 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 699 : -0.5941568 
boosting mean 699 : -0.4815351 
boosting RMSE 699 : 0.1367594 

forest 699 : -0.3893185 
forest mean 699 : -0.392066 
forest RMSE 699 : 0.05175229 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 699 : -0.2399558 
nnet mean 699 : -0.4157925 
nnet RMSE 699 : 0.1520903 


s: 700 
logit 700 : -0.4237906 
logit mean 700 : -0.4382824 
logit RMSE 700 : 0.07053958 

boosting 700 : -0.4157299 
boosting mean 700 : -0.4814411 
boosting RMSE 700 : 0.1366630 

forest 700 : -0.3425035 
forest mean 700 : -0.3919952 
forest RMSE 700 : 0.05176095 

nnet 700 : -0.5103701 
nnet mean 700 : -0.4159276 
nnet RMSE 700 : 0.1520389 


s: 701 
logit 701 : -0.4820557 
logit mean 701 : -0.4383449 
logit RMSE 701 : 0.07055735 

boosting 701 : -0.4965688 
boosting mean 701 : -0.4814627 
boosting RMSE 701 : 0.1366142 

forest 701 : -0.4074804 
forest mean 701 : -0.3920173 
forest RMSE 701 : 0.05172479 

nnet 701 : -0.4158064 
nnet mean 701 : -0.4159274 
nnet RMSE 701 : 0.1519316 


s: 702 
logit 702 : -0.5029816 
logit mean 702 : -0.4384369 
logit RMSE 702 : 0.07061413 

boosting 702 : -0.5834489 
boosting mean 702 : -0.4816079 
boosting RMSE 702 : 0.1366923 

forest 702 : -0.4218654 
forest mean 702 : -0.3920598 
forest RMSE 702 : 0.05169452 

nnet 702 : -0.6329165 
nnet mean 702 : -0.4162365 
nnet RMSE 702 : 0.1520776 


s: 703 
logit 703 : -0.4955063 
logit mean 703 : -0.4385181 
logit RMSE 703 : 0.07065577 

boosting 703 : -0.5022968 
boosting mean 703 : -0.4816374 
boosting RMSE 703 : 0.1366495 

forest 703 : -0.5030576 
forest mean 703 : -0.3922177 
forest RMSE 703 : 0.05180377 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 703 : -0.1509715 
nnet mean 703 : -0.4158592 
nnet RMSE 703 : 0.1522594 


s: 704 
logit 704 : -0.4307678 
logit mean 704 : -0.4385071 
logit RMSE 704 : 0.07061509 

boosting 704 : -0.2691367 
boosting mean 704 : -0.4813355 
boosting RMSE 704 : 0.1366415 

forest 704 : -0.4331124 
forest mean 704 : -0.3922758 
forest RMSE 704 : 0.051782 

nnet 704 : -0.4823758 
nnet mean 704 : -0.4159537 
nnet RMSE 704 : 0.1521829 


s: 705 
logit 705 : -0.4590415 
logit mean 705 : -0.4385362 
logit RMSE 705 : 0.07060002 

boosting 705 : -0.5331627 
boosting mean 705 : -0.481409 
boosting RMSE 705 : 0.1366366 

forest 705 : -0.3700197 
forest mean 705 : -0.3922443 
forest RMSE 705 : 0.05175758 

nnet 705 : -0.652933 
nnet mean 705 : -0.4162898 
nnet RMSE 705 : 0.1523730 


s: 706 
logit 706 : -0.3961046 
logit mean 706 : -0.4384761 
logit RMSE 706 : 0.07055015 

boosting 706 : -0.3587082 
boosting mean 706 : -0.4812352 
boosting RMSE 706 : 0.1365486 

forest 706 : -0.3687839 
forest mean 706 : -0.392211 
forest RMSE 706 : 0.05173426 

nnet 706 : -0.6947682 
nnet mean 706 : -0.4166842 
nnet RMSE 706 : 0.1526686 


s: 707 
logit 707 : -0.4479963 
logit mean 707 : -0.4384896 
logit RMSE 707 : 0.07052334 

boosting 707 : -0.3806019 
boosting mean 707 : -0.4810929 
boosting RMSE 707 : 0.1364540 

forest 707 : -0.4345648 
forest mean 707 : -0.3922709 
forest RMSE 707 : 0.051714 

nnet 707 : -0.3343498 
nnet mean 707 : -0.4165678 
nnet RMSE 707 : 0.1525806 


s: 708 
logit 708 : -0.3891854 
logit mean 708 : -0.43842 
logit RMSE 708 : 0.07047469 

boosting 708 : -0.4371644 
boosting mean 708 : -0.4810309 
boosting RMSE 708 : 0.1363647 

forest 708 : -0.3864196 
forest mean 708 : -0.3922627 
forest RMSE 708 : 0.05167998 

nnet 708 : -0.3607546 
nnet mean 708 : -0.416489 
nnet RMSE 708 : 0.1524799 


s: 709 
logit 709 : -0.4599153 
logit mean 709 : -0.4384503 
logit RMSE 709 : 0.07046091 

boosting 709 : -0.4308557 
boosting mean 709 : -0.4809601 
boosting RMSE 709 : 0.1362735 

forest 709 : -0.3098376 
forest mean 709 : -0.3921464 
forest RMSE 709 : 0.05175442 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 709 : -0.2060014 
nnet mean 709 : -0.4161921 
nnet RMSE 709 : 0.1525464 


s: 710 
logit 710 : -0.441011 
logit mean 710 : -0.4384539 
logit RMSE 710 : 0.0704281 

boosting 710 : -0.4923688 
boosting mean 710 : -0.4809762 
boosting RMSE 710 : 0.1362216 

forest 710 : -0.4479913 
forest mean 710 : -0.3922251 
forest RMSE 710 : 0.05174931 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 710 : -0.2319426 
nnet mean 710 : -0.4159326 
nnet RMSE 710 : 0.1525694 


s: 711 
logit 711 : -0.4587173 
logit mean 711 : -0.4384824 
logit RMSE 711 : 0.07041299 

boosting 711 : -0.5352412 
boosting mean 711 : -0.4810525 
boosting RMSE 711 : 0.1362202 

forest 711 : -0.4512189 
forest mean 711 : -0.392308 
forest RMSE 711 : 0.05174857 

nnet 711 : -0.2214744 
nnet mean 711 : -0.4156591 
nnet RMSE 711 : 0.152609 


s: 712 
logit 712 : -0.5009134 
logit mean 712 : -0.4385701 
logit RMSE 712 : 0.07046509 

boosting 712 : -0.4352067 
boosting mean 712 : -0.4809881 
boosting RMSE 712 : 0.1361309 

forest 712 : -0.4251618 
forest mean 712 : -0.3923542 
forest RMSE 712 : 0.05172081 

nnet 712 : -0.4343475 
nnet mean 712 : -0.4156853 
nnet RMSE 712 : 0.1525072 


s: 713 
logit 713 : -0.4808169 
logit mean 713 : -0.4386293 
logit RMSE 713 : 0.07048067 

boosting 713 : -0.4831242 
boosting mean 713 : -0.4809911 
boosting RMSE 713 : 0.1360710 

forest 713 : -0.4332959 
forest mean 713 : -0.3924116 
forest RMSE 713 : 0.05169957 

nnet 713 : -0.4021513 
nnet mean 713 : -0.4156663 
nnet RMSE 713 : 0.1524003 


s: 714 
logit 714 : -0.4482331 
logit mean 714 : -0.4386428 
logit RMSE 714 : 0.07045443 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 714 : -0.5719091 
boosting mean 714 : -0.4811184 
boosting RMSE 714 : 0.1361278 

forest 714 : -0.3600357 
forest mean 714 : -0.3923663 
forest RMSE 714 : 0.05168499 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 714 : -0.2769518 
nnet mean 714 : -0.4154721 
nnet RMSE 714 : 0.1523631 


s: 715 
logit 715 : -0.4785408 
logit mean 715 : -0.4386986 
logit RMSE 715 : 0.07046638 

boosting 715 : -0.4927553 
boosting mean 715 : -0.4811347 
boosting RMSE 715 : 0.1360768 

forest 715 : -0.3329832 
forest mean 715 : -0.3922832 
forest RMSE 715 : 0.05170961 

nnet 715 : -0.4054167 
nnet mean 715 : -0.415458 
nnet RMSE 715 : 0.1522567 


s: 716 
logit 716 : -0.5327582 
logit mean 716 : -0.4388299 
logit RMSE 716 : 0.07059173 

boosting 716 : -0.5083695 
boosting mean 716 : -0.4811727 
boosting RMSE 716 : 0.1360420 

forest 716 : -0.3680807 
forest mean 716 : -0.3922494 
forest RMSE 716 : 0.05168726 

nnet 716 : -0.4962628 
nnet mean 716 : -0.4155709 
nnet RMSE 716 : 0.1521928 


s: 717 
logit 717 : -0.4510021 
logit mean 717 : -0.4388469 
logit RMSE 717 : 0.07056819 

boosting 717 : -0.567805 
boosting mean 717 : -0.4812936 
boosting RMSE 717 : 0.1360915 

forest 717 : -0.4059589 
forest mean 717 : -0.3922685 
forest RMSE 717 : 0.05165168 

nnet 717 : -0.4153692 
nnet mean 717 : -0.4155706 
nnet RMSE 717 : 0.1520877 


s: 718 
logit 718 : -0.3927612 
logit mean 718 : -0.4387827 
logit RMSE 718 : 0.07051955 

boosting 718 : -0.4280779 
boosting mean 718 : -0.4812194 
boosting RMSE 718 : 0.1360007 

forest 718 : -0.4248790 
forest mean 718 : -0.3923139 
forest RMSE 718 : 0.05162405 

nnet 718 : -0.3410449 
nnet mean 718 : -0.4154668 
nnet RMSE 718 : 0.1519977 


s: 719 
logit 719 : -0.4637537 
logit mean 719 : -0.4388175 
logit RMSE 719 : 0.07051059 

boosting 719 : -0.5793238 
boosting mean 719 : -0.4813559 
boosting RMSE 719 : 0.1360706 

forest 719 : -0.3890693 
forest mean 719 : -0.3923094 
forest RMSE 719 : 0.05158975 

nnet 719 : -0.3747607 
nnet mean 719 : -0.4154102 
nnet RMSE 719 : 0.1518949 


s: 720 
logit 720 : -0.3994169 
logit mean 720 : -0.4387627 
logit RMSE 720 : 0.07046161 

boosting 720 : -0.4847481 
boosting mean 720 : -0.4813606 
boosting RMSE 720 : 0.1360127 

forest 720 : -0.3657778 
forest mean 720 : -0.3922726 
forest RMSE 720 : 0.05156968 

nnet 720 : -0.6784809 
nnet mean 720 : -0.4157755 
nnet RMSE 720 : 0.1521438 


s: 721 
logit 721 : -0.4474360 
logit mean 721 : -0.4387748 
logit RMSE 721 : 0.07043489 

boosting 721 : -0.4892862 
boosting mean 721 : -0.4813716 
boosting RMSE 721 : 0.1359590 

forest 721 : -0.4449437 
forest mean 721 : -0.3923456 
forest RMSE 721 : 0.05156108 

nnet 721 : -0.4136041 
nnet mean 721 : -0.4157725 
nnet RMSE 721 : 0.1520391 


s: 722 
logit 722 : -0.3268521 
logit mean 722 : -0.4386198 
logit RMSE 722 : 0.07043872 

boosting 722 : -0.6303368 
boosting mean 722 : -0.4815779 
boosting RMSE 722 : 0.136135 

forest 722 : -0.3761394 
forest mean 722 : -0.3923232 
forest RMSE 722 : 0.05153301 

nnet 722 : -0.5488159 
nnet mean 722 : -0.4159568 
nnet RMSE 722 : 0.1520346 


s: 723 
logit 723 : -0.531898 
logit mean 723 : -0.4387488 
logit RMSE 723 : 0.0705607 

boosting 723 : -0.5087002 
boosting mean 723 : -0.4816154 
boosting RMSE 723 : 0.1361009 

forest 723 : -0.4064578 
forest mean 723 : -0.3923427 
forest RMSE 723 : 0.05149792 

nnet 723 : -0.1917007 
nnet mean 723 : -0.4156466 
nnet RMSE 723 : 0.1521268 


s: 724 
logit 724 : -0.3528988 
logit mean 724 : -0.4386302 
logit RMSE 724 : 0.07053368 

boosting 724 : -0.3848822 
boosting mean 724 : -0.4814818 
boosting RMSE 724 : 0.136008 

forest 724 : -0.3695363 
forest mean 724 : -0.3923112 
forest RMSE 724 : 0.0514748 

nnet 724 : -0.471016 
nnet mean 724 : -0.4157231 
nnet RMSE 724 : 0.1520447 


s: 725 
logit 725 : -0.521014 
logit mean 725 : -0.4387438 
logit RMSE 725 : 0.07062816 

boosting 725 : -0.6591626 
boosting mean 725 : -0.4817269 
boosting RMSE 725 : 0.1362546 

forest 725 : -0.4330159 
forest mean 725 : -0.3923674 
forest RMSE 725 : 0.0514539 

nnet 725 : -0.432095 
nnet mean 725 : -0.4157457 
nnet RMSE 725 : 0.1519444 


s: 726 
logit 726 : -0.4568553 
logit mean 726 : -0.4387688 
logit RMSE 726 : 0.07061104 

boosting 726 : -0.5508749 
boosting mean 726 : -0.4818221 
boosting RMSE 726 : 0.1362758 

forest 726 : -0.3921626 
forest mean 726 : -0.3923671 
forest RMSE 726 : 0.05141927 

nnet 726 : -0.4936727 
nnet mean 726 : -0.415853 
nnet RMSE 726 : 0.1518795 


s: 727 
logit 727 : -0.4602332 
logit mean 727 : -0.4387983 
logit RMSE 727 : 0.07059781 

boosting 727 : -0.4680194 
boosting mean 727 : -0.4818032 
boosting RMSE 727 : 0.1362054 

forest 727 : -0.3868501 
forest mean 727 : -0.3923595 
forest RMSE 727 : 0.05138621 

nnet 727 : -0.5197061 
nnet mean 727 : -0.4159959 
nnet RMSE 727 : 0.1518400 


s: 728 
logit 728 : -0.4342792 
logit mean 728 : -0.4387921 
logit RMSE 728 : 0.07056075 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 728 : -0.3257029 
boosting mean 728 : -0.4815887 
boosting RMSE 728 : 0.1361397 

forest 728 : -0.3209737 
forest mean 728 : -0.3922615 
forest RMSE 728 : 0.05143436 

nnet 728 : -0.3325707 
nnet mean 728 : -0.4158813 
nnet RMSE 728 : 0.1517562 


s: 729 
logit 729 : -0.4561254 
logit mean 729 : -0.4388159 
logit RMSE 729 : 0.07054297 

boosting 729 : -0.5846348 
boosting mean 729 : -0.4817301 
boosting RMSE 729 : 0.136218 

forest 729 : -0.3292852 
forest mean 729 : -0.3921751 
forest RMSE 729 : 0.05146576 

nnet 729 : -0.4754790 
nnet mean 729 : -0.415963 
nnet RMSE 729 : 0.1516779 


s: 730 
logit 730 : -0.4255226 
logit mean 730 : -0.4387977 
logit RMSE 730 : 0.07050096 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 730 : -0.5662136 
boosting mean 730 : -0.4818458 
boosting RMSE 730 : 0.1362636 

forest 730 : -0.3770155 
forest mean 730 : -0.3921543 
forest RMSE 730 : 0.05143753 

nnet 730 : -0.5467249 
nnet mean 730 : -0.4161422 
nnet RMSE 730 : 0.1516712 


s: 731 
logit 731 : -0.4785389 
logit mean 731 : -0.438852 
logit RMSE 731 : 0.07051258 

boosting 731 : -0.6982333 
boosting mean 731 : -0.4821418 
boosting RMSE 731 : 0.1366164 

forest 731 : -0.3785105 
forest mean 731 : -0.3921356 
forest RMSE 731 : 0.05140848 

nnet 731 : -0.5138707 
nnet mean 731 : -0.4162758 
nnet RMSE 731 : 0.1516259 


s: 732 
logit 732 : -0.3573435 
logit mean 732 : -0.4387407 
logit RMSE 732 : 0.07048204 

boosting 732 : -0.5874391 
boosting mean 732 : -0.4822857 
boosting RMSE 732 : 0.1366987 

forest 732 : -0.4316684 
forest mean 732 : -0.3921896 
forest RMSE 732 : 0.05138669 

nnet 732 : -0.3864208 
nnet mean 732 : -0.4162351 
nnet RMSE 732 : 0.1515232 


s: 733 
logit 733 : -0.5465079 
logit mean 733 : -0.4388877 
logit RMSE 733 : 0.07064152 

boosting 733 : -0.7695037 
boosting mean 733 : -0.4826775 
boosting RMSE 733 : 0.1372855 

forest 733 : -0.3804331 
forest mean 733 : -0.3921736 
forest RMSE 733 : 0.05135671 

nnet 733 : -0.5071909 
nnet mean 733 : -0.4163591 
nnet RMSE 733 : 0.1514715 


s: 734 
logit 734 : -0.3988856 
logit mean 734 : -0.4388332 
logit RMSE 734 : 0.07059339 

boosting 734 : -0.4970255 
boosting mean 734 : -0.4826971 
boosting RMSE 734 : 0.1372387 

forest 734 : -0.3863482 
forest mean 734 : -0.3921657 
forest RMSE 734 : 0.05132419 

nnet 734 : -0.5368974 
nnet mean 734 : -0.4165234 
nnet RMSE 734 : 0.1514526 


s: 735 
logit 735 : -0.4434799 
logit mean 735 : -0.4388395 
logit RMSE 735 : 0.07056358 

boosting 735 : -0.4710634 
boosting mean 735 : -0.4826812 
boosting RMSE 735 : 0.1371704 

forest 735 : -0.4030846 
forest mean 735 : -0.3921805 
forest RMSE 735 : 0.05128939 

nnet 735 : -0.4358169 
nnet mean 735 : -0.4165496 
nnet RMSE 735 : 0.1513553 


s: 736 
logit 736 : -0.4496724 
logit mean 736 : -0.4388542 
logit RMSE 736 : 0.0705394 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 736 : -0.2552906 
boosting mean 736 : -0.4823723 
boosting RMSE 736 : 0.1371809 

forest 736 : -0.4672723 
forest mean 736 : -0.3922825 
forest RMSE 736 : 0.05131448 

nnet 736 : -0.3787586 
nnet mean 736 : -0.4164983 
nnet RMSE 736 : 0.1512545 


s: 737 
logit 737 : -0.3836371 
logit mean 737 : -0.4387793 
logit RMSE 737 : 0.0704941 

boosting 737 : -0.4310072 
boosting mean 737 : -0.4823026 
boosting RMSE 737 : 0.1370926 

forest 737 : -0.4796161 
forest mean 737 : -0.3924010 
forest RMSE 737 : 0.05136345 

nnet 737 : -0.2921733 
nnet mean 737 : -0.4163296 
nnet RMSE 737 : 0.151204 


s: 738 
logit 738 : -0.4120786 
logit mean 738 : -0.4387431 
logit RMSE 738 : 0.07044772 

boosting 738 : -0.3828941 
boosting mean 738 : -0.4821679 
boosting RMSE 738 : 0.1370011 

forest 738 : -0.4347095 
forest mean 738 : -0.3924584 
forest RMSE 738 : 0.05134454 

nnet 738 : -0.3143025 
nnet mean 738 : -0.4161913 
nnet RMSE 738 : 0.1511345 


s: 739 
logit 739 : -0.389527 
logit mean 739 : -0.4386765 
logit RMSE 739 : 0.0704011 

boosting 739 : -0.4841533 
boosting mean 739 : -0.4821706 
boosting RMSE 739 : 0.1369434 

forest 739 : -0.317007 
forest mean 739 : -0.3923563 
forest RMSE 739 : 0.05140053 

nnet 739 : -0.5242385 
nnet mean 739 : -0.4163375 
nnet RMSE 739 : 0.1511013 


s: 740 
logit 740 : -0.427022 
logit mean 740 : -0.4386608 
logit RMSE 740 : 0.07036053 

boosting 740 : -0.4315258 
boosting mean 740 : -0.4821021 
boosting RMSE 740 : 0.1368557 

forest 740 : -0.4530622 
forest mean 740 : -0.3924383 
forest RMSE 740 : 0.05140281 

nnet 740 : -0.3536508 
nnet mean 740 : -0.4162528 
nnet RMSE 740 : 0.1510088 


s: 741 
logit 741 : -0.5137142 
logit mean 741 : -0.4387621 
logit RMSE 741 : 0.07043702 

boosting 741 : -0.5047245 
boosting mean 741 : -0.4821327 
boosting RMSE 741 : 0.1368174 

forest 741 : -0.4446551 
forest mean 741 : -0.3925088 
forest RMSE 741 : 0.0513943 

nnet 741 : -0.3655738 
nnet mean 741 : -0.4161844 
nnet RMSE 741 : 0.1509121 


s: 742 
logit 742 : -0.3276538 
logit mean 742 : -0.4386123 
logit RMSE 742 : 0.07043962 

boosting 742 : -0.3069461 
boosting mean 742 : -0.4818966 
boosting RMSE 742 : 0.1367679 

forest 742 : -0.3817018 
forest mean 742 : -0.3924942 
forest RMSE 742 : 0.05136405 

nnet 742 : -0.3017126 
nnet mean 742 : -0.4160302 
nnet RMSE 742 : 0.1508536 


s: 743 
logit 743 : -0.4981899 
logit mean 743 : -0.4386925 
logit RMSE 743 : 0.07048432 

boosting 743 : -0.4487435 
boosting mean 743 : -0.4818519 
boosting RMSE 743 : 0.1366875 

forest 743 : -0.4596402 
forest mean 743 : -0.3925846 
forest RMSE 743 : 0.05137609 

nnet 743 : -0.510329 
nnet mean 743 : -0.4161571 
nnet RMSE 743 : 0.1508064 


s: 744 
logit 744 : -0.4139254 
logit mean 744 : -0.4386592 
logit RMSE 744 : 0.07043878 

boosting 744 : -0.4231726 
boosting mean 744 : -0.4817731 
boosting RMSE 744 : 0.1365982 

forest 744 : -0.3418583 
forest mean 744 : -0.3925164 
forest RMSE 744 : 0.05138578 

nnet 744 : -0.7017879 
nnet mean 744 : -0.416541 
nnet RMSE 744 : 0.1511106 


s: 745 
logit 745 : -0.3885346 
logit mean 745 : -0.4385919 
logit RMSE 745 : 0.07039274 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 745 : -0.5886724 
boosting mean 745 : -0.4819166 
boosting RMSE 745 : 0.1366814 

forest 745 : -0.3047281 
forest mean 745 : -0.3923986 
forest RMSE 745 : 0.05146977 

nnet 745 : -0.4854602 
nnet mean 745 : -0.4166335 
nnet RMSE 745 : 0.1510416 


s: 746 
logit 746 : -0.4534316 
logit mean 746 : -0.4386118 
logit RMSE 746 : 0.07037275 

boosting 746 : -0.4966284 
boosting mean 746 : -0.4819363 
boosting RMSE 746 : 0.1366356 

forest 746 : -0.3325364 
forest mean 746 : -0.3923183 
forest RMSE 746 : 0.05149454 

nnet 746 : -0.02037624 
nnet mean 746 : -0.4161023 
nnet RMSE 746 : 0.1515789 


s: 747 
logit 747 : -0.5290529 
logit mean 747 : -0.4387329 
logit RMSE 747 : 0.07048396 

boosting 747 : -0.4809476 
boosting mean 747 : -0.481935 
boosting RMSE 747 : 0.1365762 

forest 747 : -0.3854109 
forest mean 747 : -0.3923091 
forest RMSE 747 : 0.05146283 

nnet 747 : -0.6343935 
nnet mean 747 : -0.4163945 
nnet RMSE 747 : 0.1517200 


s: 748 
logit 748 : -0.3881644 
logit mean 748 : -0.4386653 
logit RMSE 748 : 0.07043816 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 748 : -0.3916115 
boosting mean 748 : -0.4818142 
boosting RMSE 748 : 0.1364853 

forest 748 : -0.3436733 
forest mean 748 : -0.3922441 
forest RMSE 748 : 0.05146963 

nnet 748 : -0.3111456 
nnet mean 748 : -0.4162538 
nnet RMSE 748 : 0.1516533 


s: 749 
logit 749 : -0.5348782 
logit mean 749 : -0.4387938 
logit RMSE 749 : 0.07056344 

boosting 749 : -0.5419246 
boosting mean 749 : -0.4818945 
boosting RMSE 749 : 0.1364927 

forest 749 : -0.3899174 
forest mean 749 : -0.3922409 
forest RMSE 749 : 0.05143658 

nnet 749 : -0.4685149 
nnet mean 749 : -0.4163236 
nnet RMSE 749 : 0.1515727 


s: 750 
logit 750 : -0.3203017 
logit mean 750 : -0.4386358 
logit RMSE 750 : 0.0705764 

boosting 750 : -0.4282775 
boosting mean 750 : -0.481823 
boosting RMSE 750 : 0.1364056 

forest 750 : -0.2933002 
forest mean 750 : -0.392109 
forest RMSE 750 : 0.05154973 

nnet 750 : -0.7402319 
nnet mean 750 : -0.4167555 
nnet RMSE 750 : 0.1519803 


s: 751 
logit 751 : -0.3735807 
logit mean 751 : -0.4385492 
logit RMSE 751 : 0.07053599 

boosting 751 : -0.5981781 
boosting mean 751 : -0.4819779 
boosting RMSE 751 : 0.1365064 

forest 751 : -0.3912132 
forest mean 751 : -0.3921078 
forest RMSE 751 : 0.05151639 

nnet 751 : -0.5037471 
nnet mean 751 : -0.4168713 
nnet RMSE 751 : 0.1519262 


s: 752 
logit 752 : -0.4336245 
logit mean 752 : -0.4385426 
logit RMSE 752 : 0.07049974 

boosting 752 : -0.478748 
boosting mean 752 : -0.4819736 
boosting RMSE 752 : 0.1364458 

forest 752 : -0.3974069 
forest mean 752 : -0.3921149 
forest RMSE 752 : 0.05148221 

nnet 752 : -0.2422273 
nnet mean 752 : -0.4166391 
nnet RMSE 752 : 0.1519341 


s: 753 
logit 753 : -0.4793557 
logit mean 753 : -0.4385968 
logit RMSE 753 : 0.07051224 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 753 : -0.4541099 
boosting mean 753 : -0.4819366 
boosting RMSE 753 : 0.1363694 

forest 753 : -0.4659779 
forest mean 753 : -0.392213 
forest RMSE 753 : 0.05150417 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 753 : -0.2992383 
nnet mean 753 : -0.4164832 
nnet RMSE 753 : 0.1518776 


s: 754 
logit 754 : -0.4048181 
logit mean 754 : -0.438552 
logit RMSE 754 : 0.07046568 

boosting 754 : -0.4140547 
boosting mean 754 : -0.4818466 
boosting RMSE 754 : 0.1362799 

forest 754 : -0.3705196 
forest mean 754 : -0.3921842 
forest RMSE 754 : 0.0514812 

nnet 754 : -0.4044529 
nnet mean 754 : -0.4164672 
nnet RMSE 754 : 0.1517770 


s: 755 
logit 755 : -0.4957741 
logit mean 755 : -0.4386278 
logit RMSE 755 : 0.07050521 

boosting 755 : -0.534101 
boosting mean 755 : -0.4819158 
boosting RMSE 755 : 0.1362771 

forest 755 : -0.4290108 
forest mean 755 : -0.392233 
forest RMSE 755 : 0.05145793 

nnet 755 : -0.3374442 
nnet mean 755 : -0.4163626 
nnet RMSE 755 : 0.1516935 


s: 756 
logit 756 : -0.5552884 
logit mean 756 : -0.4387821 
logit RMSE 756 : 0.07068456 

boosting 756 : -0.7257004 
boosting mean 756 : -0.4822383 
boosting RMSE 756 : 0.1367011 

forest 756 : -0.3282728 
forest mean 756 : -0.3921484 
forest RMSE 756 : 0.05149001 

nnet 756 : -0.6864286 
nnet mean 756 : -0.4167198 
nnet RMSE 756 : 0.1519506 


s: 757 
logit 757 : -0.4788859 
logit mean 757 : -0.4388351 
logit RMSE 757 : 0.07069602 

boosting 757 : -0.5913621 
boosting mean 757 : -0.4823824 
boosting RMSE 757 : 0.1367877 

forest 757 : -0.3328383 
forest mean 757 : -0.39207 
forest RMSE 757 : 0.05151386 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 757 : -0.2416233 
nnet mean 757 : -0.4164885 
nnet RMSE 757 : 0.1519593 


s: 758 
logit 758 : -0.4589287 
logit mean 758 : -0.4388616 
logit RMSE 758 : 0.07068179 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 758 : -0.3955194 
boosting mean 758 : -0.4822678 
boosting RMSE 758 : 0.1366976 

forest 758 : -0.4046775 
forest mean 758 : -0.3920867 
forest RMSE 758 : 0.05148015 

nnet 758 : -0.3492233 
nnet mean 758 : -0.4163997 
nnet RMSE 758 : 0.1518702 


s: 759 
logit 759 : -0.4180884 
logit mean 759 : -0.4388342 
logit RMSE 759 : 0.07063826 

boosting 759 : -0.4717607 
boosting mean 759 : -0.482254 
boosting RMSE 759 : 0.1366323 

forest 759 : -0.4449525 
forest mean 759 : -0.3921563 
forest RMSE 759 : 0.05147209 

nnet 759 : -0.726999 
nnet mean 759 : -0.416809 
nnet RMSE 759 : 0.1522336 


s: 760 
logit 760 : -0.4418648 
logit mean 760 : -0.4388382 
logit RMSE 760 : 0.0706081 

boosting 760 : -0.6220886 
boosting mean 760 : -0.482438 
boosting RMSE 760 : 0.1367799 

forest 760 : -0.3599476 
forest mean 760 : -0.3921139 
forest RMSE 760 : 0.05145873 

nnet 760 : -0.3056226 
nnet mean 760 : -0.4166627 
nnet RMSE 760 : 0.1521719 


s: 761 
logit 761 : -0.4718072 
logit mean 761 : -0.4388815 
logit RMSE 761 : 0.0706097 

boosting 761 : -0.5686106 
boosting mean 761 : -0.4825512 
boosting RMSE 761 : 0.1368265 

forest 761 : -0.3564885 
forest mean 761 : -0.3920671 
forest RMSE 761 : 0.05144909 

nnet 761 : -0.6205741 
nnet mean 761 : -0.4169306 
nnet RMSE 761 : 0.1522820 


s: 762 
logit 762 : -0.4296449 
logit mean 762 : -0.4388694 
logit RMSE 762 : 0.07057152 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 762 : -0.5764689 
boosting mean 762 : -0.4826745 
boosting RMSE 762 : 0.1368861 

forest 762 : -0.3971071 
forest mean 762 : -0.3920737 
forest RMSE 762 : 0.05141543 

nnet 762 : -0.4996963 
nnet mean 762 : -0.4170392 
nnet RMSE 762 : 0.1522248 


s: 763 
logit 763 : -0.4361824 
logit mean 763 : -0.4388659 
logit RMSE 763 : 0.07053742 

boosting 763 : -0.4394419 
boosting mean 763 : -0.4826178 
boosting RMSE 763 : 0.1368038 

forest 763 : -0.4637046 
forest mean 763 : -0.3921676 
forest RMSE 763 : 0.05143346 

nnet 763 : -0.3558728 
nnet mean 763 : -0.4169591 
nnet RMSE 763 : 0.1521335 


s: 764 
logit 764 : -0.3531466 
logit mean 764 : -0.4387537 
logit RMSE 764 : 0.07051162 

boosting 764 : -0.4022376 
boosting mean 764 : -0.4825126 
boosting RMSE 764 : 0.1367143 

forest 764 : -0.3300155 
forest mean 764 : -0.3920863 
forest RMSE 764 : 0.05146211 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 764 : -0.1977119 
nnet mean 764 : -0.4166721 
nnet RMSE 764 : 0.1522099 


s: 765 
logit 765 : -0.4182193 
logit mean 765 : -0.4387269 
logit RMSE 765 : 0.0704686 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 765 : -0.5898286 
boosting mean 765 : -0.4826529 
boosting RMSE 765 : 0.1367972 

forest 765 : -0.3882695 
forest mean 765 : -0.3920813 
forest RMSE 765 : 0.05143021 

nnet 765 : -0.6368542 
nnet mean 765 : -0.4169599 
nnet RMSE 765 : 0.1523512 


s: 766 
logit 766 : -0.3076129 
logit mean 766 : -0.4385557 
logit RMSE 766 : 0.07050166 

boosting 766 : -0.5939099 
boosting mean 766 : -0.4827981 
boosting RMSE 766 : 0.1368873 

forest 766 : -0.456076 
forest mean 766 : -0.3921648 
forest RMSE 766 : 0.05143655 

nnet 766 : -0.3574324 
nnet mean 766 : -0.4168822 
nnet RMSE 766 : 0.1522595 


s: 767 
logit 767 : -0.4898241 
logit mean 767 : -0.4386225 
logit RMSE 767 : 0.0705303 

boosting 767 : -0.4629133 
boosting mean 767 : -0.4827722 
boosting RMSE 767 : 0.1368169 

forest 767 : -0.3982444 
forest mean 767 : -0.3921727 
forest RMSE 767 : 0.05140305 

nnet 767 : -0.4616105 
nnet mean 767 : -0.4169405 
nnet RMSE 767 : 0.1521765 


s: 768 
logit 768 : -0.4585123 
logit mean 768 : -0.4386484 
logit RMSE 768 : 0.07051598 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 768 : -0.4581268 
boosting mean 768 : -0.4827401 
boosting RMSE 768 : 0.1367438 

forest 768 : -0.4259977 
forest mean 768 : -0.3922168 
forest RMSE 768 : 0.05137814 

nnet 768 : -0.3681119 
nnet mean 768 : -0.4168769 
nnet RMSE 768 : 0.1520818 


s: 769 
logit 769 : -0.459978 
logit mean 769 : -0.4386762 
logit RMSE 769 : 0.0705033 

boosting 769 : -0.5036405 
boosting mean 769 : -0.4827673 
boosting RMSE 769 : 0.136706 

forest 769 : -0.3928306 
forest mean 769 : -0.3922176 
forest RMSE 769 : 0.05134537 

nnet 769 : -0.2075401 
nnet mean 769 : -0.4166047 
nnet RMSE 769 : 0.1521412 


s: 770 
logit 770 : -0.479555 
logit mean 770 : -0.4387293 
logit RMSE 770 : 0.0705158 

boosting 770 : -0.5495017 
boosting mean 770 : -0.4828539 
boosting RMSE 770 : 0.1367234 

forest 770 : -0.5504601 
forest mean 770 : -0.3924231 
forest RMSE 770 : 0.05159771 

nnet 770 : -0.2596932 
nnet mean 770 : -0.4164009 
nnet RMSE 770 : 0.1521264 


s: 771 
logit 771 : -0.5026524 
logit mean 771 : -0.4388122 
logit RMSE 771 : 0.07056697 

boosting 771 : -0.5999869 
boosting mean 771 : -0.4830059 
boosting RMSE 771 : 0.1368244 

forest 771 : -0.405416 
forest mean 771 : -0.3924399 
forest RMSE 771 : 0.05156461 

nnet 771 : -0.6071725 
nnet mean 771 : -0.4166484 
nnet RMSE 771 : 0.1522107 


s: 772 
logit 772 : -0.4831022 
logit mean 772 : -0.4388695 
logit RMSE 772 : 0.07058464 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 772 : -0.6639638 
boosting mean 772 : -0.4832403 
boosting RMSE 772 : 0.1370654 

forest 772 : -0.3869577 
forest mean 772 : -0.3924328 
forest RMSE 772 : 0.05153334 

nnet 772 : -0.1061729 
nnet mean 772 : -0.4162462 
nnet RMSE 772 : 0.1524793 


s: 773 
logit 773 : -0.3956703 
logit mean 773 : -0.4388136 
logit RMSE 773 : 0.07053915 

boosting 773 : -0.4845518 
boosting mean 773 : -0.483242 
boosting RMSE 773 : 0.1370105 

forest 773 : -0.3725098 
forest mean 773 : -0.3924071 
forest RMSE 773 : 0.05150948 

nnet 773 : -0.3984359 
nnet mean 773 : -0.4162232 
nnet RMSE 773 : 0.1523806 


s: 774 
logit 774 : -0.3910699 
logit mean 774 : -0.438752 
logit RMSE 774 : 0.0704943 

boosting 774 : -0.3641651 
boosting mean 774 : -0.4830881 
boosting RMSE 774 : 0.1369280 

forest 774 : -0.3537109 
forest mean 774 : -0.3923571 
forest RMSE 774 : 0.05150308 

nnet 774 : -0.1747791 
nnet mean 774 : -0.4159112 
nnet RMSE 774 : 0.1524972 


s: 775 
logit 775 : -0.3993812 
logit mean 775 : -0.4387012 
logit RMSE 775 : 0.0704488 

boosting 775 : -0.3557202 
boosting mean 775 : -0.4829238 
boosting RMSE 775 : 0.1368488 

forest 775 : -0.3801145 
forest mean 775 : -0.3923413 
forest RMSE 775 : 0.0514748 

nnet 775 : -0.3222205 
nnet mean 775 : -0.4157903 
nnet RMSE 775 : 0.1524244 


s: 776 
logit 776 : -0.3811903 
logit mean 776 : -0.4386270 
logit RMSE 776 : 0.07040663 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 776 : -0.4885576 
boosting mean 776 : -0.482931 
boosting RMSE 776 : 0.1367976 

forest 776 : -0.3602846 
forest mean 776 : -0.3923 
forest RMSE 776 : 0.05146137 

nnet 776 : -0.4821618 
nnet mean 776 : -0.4158759 
nnet RMSE 776 : 0.1523547 


s: 777 
logit 777 : -0.5014117 
logit mean 777 : -0.4387079 
logit RMSE 777 : 0.0704553 

boosting 777 : -0.509692 
boosting mean 777 : -0.4829655 
boosting RMSE 777 : 0.1367662 

forest 777 : -0.3719007 
forest mean 777 : -0.3922737 
forest RMSE 777 : 0.05143813 

nnet 777 : -0.5444169 
nnet mean 777 : -0.4160413 
nnet RMSE 777 : 0.1523447 


s: 778 
logit 778 : -0.3994859 
logit mean 778 : -0.4386574 
logit RMSE 778 : 0.07041001 

boosting 778 : -0.4081077 
boosting mean 778 : -0.4828693 
boosting RMSE 778 : 0.1366785 

forest 778 : -0.3394667 
forest mean 778 : -0.3922058 
forest RMSE 778 : 0.05145085 

nnet 778 : -0.3519224 
nnet mean 778 : -0.4159589 
nnet RMSE 778 : 0.1522566 


s: 779 
logit 779 : -0.4547884 
logit mean 779 : -0.4386781 
logit RMSE 779 : 0.07039218 

boosting 779 : -0.5898801 
boosting mean 779 : -0.4830066 
boosting RMSE 779 : 0.1367601 

forest 779 : -0.4108012 
forest mean 779 : -0.3922297 
forest RMSE 779 : 0.05141927 

nnet 779 : -0.5072234 
nnet mean 779 : -0.416076 
nnet RMSE 779 : 0.1522073 


s: 780 
logit 780 : -0.4548017 
logit mean 780 : -0.4386988 
logit RMSE 780 : 0.0703744 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 780 : -0.5279224 
boosting mean 780 : -0.4830642 
boosting RMSE 780 : 0.1367491 

forest 780 : -0.3824580 
forest mean 780 : -0.3922172 
forest RMSE 780 : 0.05139014 

nnet 780 : -0.5108384 
nnet mean 780 : -0.4161975 
nnet RMSE 780 : 0.1521614 


s: 781 
logit 781 : -0.4315926 
logit mean 781 : -0.4386897 
logit RMSE 781 : 0.07033842 

boosting 781 : -0.55064 
boosting mean 781 : -0.4831507 
boosting RMSE 781 : 0.1367678 

forest 781 : -0.4474688 
forest mean 781 : -0.3922879 
forest RMSE 781 : 0.05138531 

nnet 781 : -0.4030015 
nnet mean 781 : -0.4161806 
nnet RMSE 781 : 0.1520640 


s: 782 
logit 782 : -0.4157529 
logit mean 782 : -0.4386604 
logit RMSE 782 : 0.07029569 

boosting 782 : -0.6267083 
boosting mean 782 : -0.4833343 
boosting RMSE 782 : 0.1369206 

forest 782 : -0.3646305 
forest mean 782 : -0.3922526 
forest RMSE 782 : 0.05136802 

nnet 782 : -0.03272129 
nnet mean 782 : -0.4156903 
nnet RMSE 782 : 0.1525333 


s: 783 
logit 783 : -0.4088776 
logit mean 783 : -0.4386224 
logit RMSE 783 : 0.0702515 

boosting 783 : -0.464445 
boosting mean 783 : -0.4833102 
boosting RMSE 783 : 0.1368525 

forest 783 : -0.4435827 
forest mean 783 : -0.3923181 
forest RMSE 783 : 0.05135883 

nnet 783 : -0.5386703 
nnet mean 783 : -0.4158473 
nnet RMSE 783 : 0.1525164 


s: 784 
logit 784 : -0.3961821 
logit mean 784 : -0.4385682 
logit RMSE 784 : 0.07020682 

boosting 784 : -0.4085575 
boosting mean 784 : -0.4832148 
boosting RMSE 784 : 0.1367655 

forest 784 : -0.3698488 
forest mean 784 : -0.3922895 
forest RMSE 784 : 0.05133736 

nnet 784 : -0.5355557 
nnet mean 784 : -0.416 
nnet RMSE 784 : 0.1524959 


s: 785 
logit 785 : -0.4821215 
logit mean 785 : -0.4386237 
logit RMSE 785 : 0.07022328 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 785 : -0.575434 
boosting mean 785 : -0.4833323 
boosting RMSE 785 : 0.1368217 

forest 785 : -0.4744256 
forest mean 785 : -0.3923941 
forest RMSE 785 : 0.05137337 

nnet 785 : -0.6081389 
nnet mean 785 : -0.4162448 
nnet RMSE 785 : 0.1525797 


s: 786 
logit 786 : -0.500792 
logit mean 786 : -0.4387028 
logit RMSE 786 : 0.07027063 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 786 : -0.2389713 
boosting mean 786 : -0.4830214 
boosting RMSE 786 : 0.1368553 

forest 786 : -0.39964 
forest mean 786 : -0.3924033 
forest RMSE 786 : 0.05134068 

nnet 786 : -0.3431196 
nnet mean 786 : -0.4161517 
nnet RMSE 786 : 0.1524961 


s: 787 
logit 787 : -0.5300176 
logit mean 787 : -0.4388188 
logit RMSE 787 : 0.07037873 

boosting 787 : -0.4868792 
boosting mean 787 : -0.4830263 
boosting RMSE 787 : 0.1368033 

forest 787 : -0.3720321 
forest mean 787 : -0.3923774 
forest RMSE 787 : 0.05131774 

nnet 787 : -0.3993467 
nnet mean 787 : -0.4161304 
nnet RMSE 787 : 0.1523992 


s: 788 
logit 788 : -0.4438835 
logit mean 788 : -0.4388253 
logit RMSE 788 : 0.07035143 

boosting 788 : -0.5884457 
boosting mean 788 : -0.4831601 
boosting RMSE 788 : 0.1368812 

forest 788 : -0.354444 
forest mean 788 : -0.3923293 
forest RMSE 788 : 0.05131083 

nnet 788 : -0.1662475 
nnet mean 788 : -0.4158133 
nnet RMSE 788 : 0.1525300 


s: 789 
logit 789 : -0.4766780 
logit mean 789 : -0.4388732 
logit RMSE 789 : 0.07035981 

boosting 789 : -0.461424 
boosting mean 789 : -0.4831326 
boosting RMSE 789 : 0.1368119 

forest 789 : -0.3937813 
forest mean 789 : -0.3923311 
forest RMSE 789 : 0.05127879 

nnet 789 : -0.5049918 
nnet mean 789 : -0.4159263 
nnet RMSE 789 : 0.1524791 


s: 790 
logit 790 : -0.4293504 
logit mean 790 : -0.4388612 
logit RMSE 790 : 0.07032302 

boosting 790 : -0.5289333 
boosting mean 790 : -0.4831905 
boosting RMSE 790 : 0.1368022 

forest 790 : -0.5055777 
forest mean 790 : -0.3924745 
forest RMSE 790 : 0.0513838 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 790 : -0.2715635 
nnet mean 790 : -0.4157436 
nnet RMSE 790 : 0.1524511 


s: 791 
logit 791 : -0.4572229 
logit mean 791 : -0.4388844 
logit RMSE 791 : 0.070308 

boosting 791 : -0.3016967 
boosting mean 791 : -0.4829611 
boosting RMSE 791 : 0.1367604 

forest 791 : -0.3753892 
forest mean 791 : -0.3924529 
forest RMSE 791 : 0.05135877 

nnet 791 : -0.5770247 
nnet mean 791 : -0.4159475 
nnet RMSE 791 : 0.1524846 


s: 792 
logit 792 : -0.3937952 
logit mean 792 : -0.4388275 
logit RMSE 792 : 0.07026395 

boosting 792 : -0.4695378 
boosting mean 792 : -0.4829441 
boosting RMSE 792 : 0.1366964 

forest 792 : -0.329605 
forest mean 792 : -0.3923735 
forest RMSE 792 : 0.05138725 

nnet 792 : -0.4372867 
nnet mean 792 : -0.4159744 
nnet RMSE 792 : 0.1523941 


s: 793 
logit 793 : -0.3842106 
logit mean 793 : -0.4387586 
logit RMSE 793 : 0.07022187 

boosting 793 : -0.4734143 
boosting mean 793 : -0.4829321 
boosting RMSE 793 : 0.1366350 

forest 793 : -0.3175578 
forest mean 793 : -0.3922792 
forest RMSE 793 : 0.05143822 

nnet 793 : -0.6352764 
nnet mean 793 : -0.416251 
nnet RMSE 793 : 0.1525270 


s: 794 
logit 794 : -0.4744393 
logit mean 794 : -0.4388035 
logit RMSE 794 : 0.07022734 

boosting 794 : -0.4982037 
boosting mean 794 : -0.4829514 
boosting RMSE 794 : 0.1365934 

forest 794 : -0.4259897 
forest mean 794 : -0.3923216 
forest RMSE 794 : 0.05141409 

nnet 794 : -0.540865 
nnet mean 794 : -0.4164079 
nnet RMSE 794 : 0.1525128 


s: 795 
logit 795 : -0.4595479 
logit mean 795 : -0.4388296 
logit RMSE 795 : 0.07021493 

boosting 795 : -0.4232545 
boosting mean 795 : -0.4828763 
boosting RMSE 795 : 0.13651 

forest 795 : -0.3757272 
forest mean 795 : -0.3923008 
forest RMSE 795 : 0.05138895 

nnet 795 : -0.440169 
nnet mean 795 : -0.4164378 
nnet RMSE 795 : 0.1524236 


s: 796 
logit 796 : -0.4322571 
logit mean 796 : -0.4388214 
logit RMSE 796 : 0.07018012 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 796 : -0.2370734 
boosting mean 796 : -0.4825675 
boosting RMSE 796 : 0.1365464 

forest 796 : -0.3364038 
forest mean 796 : -0.3922305 
forest RMSE 796 : 0.05140611 

nnet 796 : -0.1907821 
nnet mean 796 : -0.4161543 
nnet RMSE 796 : 0.1525082 


s: 797 
logit 797 : -0.4183263 
logit mean 797 : -0.4387956 
logit RMSE 797 : 0.07013908 

boosting 797 : -0.525641 
boosting mean 797 : -0.4826215 
boosting RMSE 797 : 0.1365332 

forest 797 : -0.4256651 
forest mean 797 : -0.3922725 
forest RMSE 797 : 0.05138189 

nnet 797 : -0.3977786 
nnet mean 797 : -0.4161313 
nnet RMSE 797 : 0.1524125 


s: 798 
logit 798 : -0.3929539 
logit mean 798 : -0.4387382 
logit RMSE 798 : 0.07009557 

boosting 798 : -0.4208328 
boosting mean 798 : -0.4825441 
boosting RMSE 798 : 0.1364497 

forest 798 : -0.4299395 
forest mean 798 : -0.3923197 
forest RMSE 798 : 0.05136062 

nnet 798 : -0.4314225 
nnet mean 798 : -0.4161504 
nnet RMSE 798 : 0.1523210 


s: 799 
logit 799 : -0.4135765 
logit mean 799 : -0.4387067 
logit RMSE 799 : 0.07005333 

boosting 799 : -0.3733007 
boosting mean 799 : -0.4824074 
boosting RMSE 799 : 0.1363675 

forest 799 : -0.2944739 
forest mean 799 : -0.3921972 
forest RMSE 799 : 0.05146406 

nnet 799 : -0.3921895 
nnet mean 799 : -0.4161204 
nnet RMSE 799 : 0.1522259 


s: 800 
logit 800 : -0.4076096 
logit mean 800 : -0.4386678 
logit RMSE 800 : 0.07001005 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 800 : -0.6776742 
boosting mean 800 : -0.4826514 
boosting RMSE 800 : 0.1366354 

forest 800 : -0.4466883 
forest mean 800 : -0.3922653 
forest RMSE 800 : 0.05145836 

nnet 800 : -0.6248025 
nnet mean 800 : -0.4163813 
nnet RMSE 800 : 0.1523382 


s: 801 
logit 801 : -0.4850338 
logit mean 801 : -0.4387257 
logit RMSE 801 : 0.07003082 

boosting 801 : -0.5147713 
boosting mean 801 : -0.4826915 
boosting RMSE 801 : 0.1366103 

forest 801 : -0.3902636 
forest mean 801 : -0.3922628 
forest RMSE 801 : 0.05142738 

nnet 801 : -0.794546 
nnet mean 801 : -0.4168534 
nnet RMSE 801 : 0.1528800 


s: 802 
logit 802 : -0.5163143 
logit mean 802 : -0.4388225 
logit RMSE 802 : 0.07010756 

boosting 802 : -0.5360787 
boosting mean 802 : -0.4827581 
boosting RMSE 802 : 0.1366096 

forest 802 : -0.4094449 
forest mean 802 : -0.3922843 
forest RMSE 802 : 0.05139639 

nnet 802 : -0.614384 
nnet mean 802 : -0.4170997 
nnet RMSE 802 : 0.1529721 


s: 803 
logit 803 : -0.4027468 
logit mean 803 : -0.4387775 
logit RMSE 803 : 0.07006396 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 803 : -0.4957345 
boosting mean 803 : -0.4827743 
boosting RMSE 803 : 0.1365663 

forest 803 : -0.3706318 
forest mean 803 : -0.3922573 
forest RMSE 803 : 0.05137484 

nnet 803 : -0.344889 
nnet mean 803 : -0.4170098 
nnet RMSE 803 : 0.1528892 


s: 804 
logit 804 : -0.5933214 
logit mean 804 : -0.4389698 
logit RMSE 804 : 0.07035152 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 804 : -0.4624315 
boosting mean 804 : -0.482749 
boosting RMSE 804 : 0.1364991 

forest 804 : -0.3490290 
forest mean 804 : -0.3922035 
forest RMSE 804 : 0.05137434 

nnet 804 : -0.4608975 
nnet mean 804 : -0.4170643 
nnet RMSE 804 : 0.1528092 


s: 805 
logit 805 : -0.4698646 
logit mean 805 : -0.4390081 
logit RMSE 805 : 0.07035092 

boosting 805 : -0.5194858 
boosting mean 805 : -0.4827946 
boosting RMSE 805 : 0.1364793 

forest 805 : -0.4815402 
forest mean 805 : -0.3923145 
forest RMSE 805 : 0.05142279 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 805 : -0.2234214 
nnet mean 805 : -0.4168238 
nnet RMSE 805 : 0.152841 


s: 806 
logit 806 : -0.4129987 
logit mean 806 : -0.4389759 
logit RMSE 806 : 0.07030875 

boosting 806 : -0.4909546 
boosting mean 806 : -0.4828047 
boosting RMSE 806 : 0.1364323 

forest 806 : -0.4670606 
forest mean 806 : -0.3924073 
forest RMSE 806 : 0.05144513 

nnet 806 : -0.2049919 
nnet mean 806 : -0.416561 
nnet RMSE 806 : 0.1529005 


s: 807 
logit 807 : -0.3990729 
logit mean 807 : -0.4389264 
logit RMSE 807 : 0.07026519 

boosting 807 : -0.4889897 
boosting mean 807 : -0.4828124 
boosting RMSE 807 : 0.1363837 

forest 807 : -0.4210903 
forest mean 807 : -0.3924428 
forest RMSE 807 : 0.05141861 

nnet 807 : -0.338727 
nnet mean 807 : -0.4164645 
nnet RMSE 807 : 0.152821 


s: 808 
logit 808 : -0.3780674 
logit mean 808 : -0.4388511 
logit RMSE 808 : 0.07022593 

boosting 808 : -0.3935122 
boosting mean 808 : -0.4827019 
boosting RMSE 808 : 0.1362994 

forest 808 : -0.3673565 
forest mean 808 : -0.3924117 
forest RMSE 808 : 0.05139961 

nnet 808 : -0.4409336 
nnet mean 808 : -0.4164948 
nnet RMSE 808 : 0.1527332 


s: 809 
logit 809 : -0.5083206 
logit mean 809 : -0.438937 
logit RMSE 809 : 0.07028577 

boosting 809 : -0.3601172 
boosting mean 809 : -0.4825503 
boosting RMSE 809 : 0.1362224 

forest 809 : -0.3514323 
forest mean 809 : -0.3923611 
forest RMSE 809 : 0.05139621 

nnet 809 : -0.5800733 
nnet mean 809 : -0.416697 
nnet RMSE 809 : 0.15277 


s: 810 
logit 810 : -0.4412821 
logit mean 810 : -0.4389399 
logit RMSE 810 : 0.07025734 

boosting 810 : -0.5095616 
boosting mean 810 : -0.4825837 
boosting RMSE 810 : 0.1361927 

forest 810 : -0.4288868 
forest mean 810 : -0.3924062 
forest RMSE 810 : 0.0513745 

nnet 810 : -0.1765859 
nnet mean 810 : -0.4164006 
nnet RMSE 810 : 0.1528773 


s: 811 
logit 811 : -0.5431499 
logit mean 811 : -0.4390684 
logit RMSE 811 : 0.07039371 

boosting 811 : -0.438324 
boosting mean 811 : -0.4825291 
boosting RMSE 811 : 0.1361154 

forest 811 : -0.4101825 
forest mean 811 : -0.3924281 
forest RMSE 811 : 0.05134406 

nnet 811 : -0.3481942 
nnet mean 811 : -0.4163165 
nnet RMSE 811 : 0.1527939 


s: 812 
logit 812 : -0.5145964 
logit mean 812 : -0.4391614 
logit RMSE 812 : 0.0704652 

boosting 812 : -0.4178114 
boosting mean 812 : -0.4824494 
boosting RMSE 812 : 0.1360330 

forest 812 : -0.4151316 
forest mean 812 : -0.3924561 
forest RMSE 812 : 0.05131518 

nnet 812 : -0.3281884 
nnet mean 812 : -0.4162079 
nnet RMSE 812 : 0.1527206 


s: 813 
logit 813 : -0.478592 
logit mean 813 : -0.4392099 
logit RMSE 813 : 0.07047578 

boosting 813 : -0.7124623 
boosting mean 813 : -0.4827323 
boosting RMSE 813 : 0.1363902 

forest 813 : -0.4724621 
forest mean 813 : -0.3925545 
forest RMSE 813 : 0.05134654 

nnet 813 : -0.3305335 
nnet mean 813 : -0.4161026 
nnet RMSE 813 : 0.1526461 


s: 814 
logit 814 : -0.4592385 
logit mean 814 : -0.4392345 
logit RMSE 814 : 0.07046307 

boosting 814 : -0.513193 
boosting mean 814 : -0.4827698 
boosting RMSE 814 : 0.1363641 

forest 814 : -0.3728997 
forest mean 814 : -0.3925303 
forest RMSE 814 : 0.05132379 

nnet 814 : -0.5014712 
nnet mean 814 : -0.4162074 
nnet RMSE 814 : 0.1525937 


s: 815 
logit 815 : -0.4205866 
logit mean 815 : -0.4392116 
logit RMSE 815 : 0.07042352 

boosting 815 : -0.4508322 
boosting mean 815 : -0.4827306 
boosting RMSE 815 : 0.1362921 

forest 815 : -0.4108432 
forest mean 815 : -0.3925528 
forest RMSE 815 : 0.0512937 

nnet 815 : -0.5568297 
nnet mean 815 : -0.41638 
nnet RMSE 815 : 0.152599 


s: 816 
logit 816 : -0.5229721 
logit mean 816 : -0.4393142 
logit RMSE 816 : 0.07051189 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 816 : -0.6246737 
boosting mean 816 : -0.4829045 
boosting RMSE 816 : 0.1364354 

forest 816 : -0.4929239 
forest mean 816 : -0.3926758 
forest RMSE 816 : 0.05136537 

nnet 816 : -0.4994238 
nnet mean 816 : -0.4164818 
nnet RMSE 816 : 0.1525452 


s: 817 
logit 817 : -0.4041805 
logit mean 817 : -0.4392712 
logit RMSE 817 : 0.07046888 

boosting 817 : -0.5816896 
boosting mean 817 : -0.4830254 
boosting RMSE 817 : 0.1365 

forest 817 : -0.3502989 
forest mean 817 : -0.3926239 
forest RMSE 817 : 0.05136336 

nnet 817 : -0.4550752 
nnet mean 817 : -0.416529 
nnet RMSE 817 : 0.1524640 


s: 818 
logit 818 : -0.3840259 
logit mean 818 : -0.4392037 
logit RMSE 818 : 0.070428 

boosting 818 : -0.5088565 
boosting mean 818 : -0.483057 
boosting RMSE 818 : 0.1364696 

forest 818 : -0.4048188 
forest mean 818 : -0.3926388 
forest RMSE 818 : 0.05133223 

nnet 818 : -0.5629933 
nnet mean 818 : -0.4167080 
nnet RMSE 818 : 0.1524773 


s: 819 
logit 819 : -0.5292229 
logit mean 819 : -0.4393136 
logit RMSE 819 : 0.07052968 

boosting 819 : -0.5304393 
boosting mean 819 : -0.4831149 
boosting RMSE 819 : 0.1364624 

forest 819 : -0.3800148 
forest mean 819 : -0.3926234 
forest RMSE 819 : 0.05130564 

nnet 819 : -0.4437195 
nnet mean 819 : -0.416741 
nnet RMSE 819 : 0.1523918 


s: 820 
logit 820 : -0.4750701 
logit mean 820 : -0.4393572 
logit RMSE 820 : 0.0705354 

boosting 820 : -0.5160649 
boosting mean 820 : -0.483155 
boosting RMSE 820 : 0.1364394 

forest 820 : -0.4449876 
forest mean 820 : -0.3926873 
forest RMSE 820 : 0.05129841 

nnet 820 : -0.3344252 
nnet mean 820 : -0.4166406 
nnet RMSE 820 : 0.1523161 


s: 821 
logit 821 : -0.4860875 
logit mean 821 : -0.4394141 
logit RMSE 821 : 0.07055643 

boosting 821 : -0.4834242 
boosting mean 821 : -0.4831554 
boosting RMSE 821 : 0.1363874 

forest 821 : -0.3752965 
forest mean 821 : -0.3926661 
forest RMSE 821 : 0.0512744 

nnet 821 : -0.3793182 
nnet mean 821 : -0.4165952 
nnet RMSE 821 : 0.152225 


s: 822 
logit 822 : -0.5422623 
logit mean 822 : -0.4395393 
logit RMSE 822 : 0.07068787 

boosting 822 : -0.4843288 
boosting mean 822 : -0.4831568 
boosting RMSE 822 : 0.1363361 

forest 822 : -0.4499372 
forest mean 822 : -0.3927358 
forest RMSE 822 : 0.0512728 

nnet 822 : -0.2269514 
nnet mean 822 : -0.4163645 
nnet RMSE 822 : 0.1522521 


s: 823 
logit 823 : -0.4165348 
logit mean 823 : -0.4395113 
logit RMSE 823 : 0.07064726 

boosting 823 : -0.7573815 
boosting mean 823 : -0.48349 
boosting RMSE 823 : 0.1368216 

forest 823 : -0.4254248 
forest mean 823 : -0.3927755 
forest RMSE 823 : 0.0512493 

nnet 823 : -0.4961362 
nnet mean 823 : -0.4164614 
nnet RMSE 823 : 0.1521964 


s: 824 
logit 824 : -0.3971063 
logit mean 824 : -0.4394598 
logit RMSE 824 : 0.07060445 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 824 : -0.5913191 
boosting mean 824 : -0.4836209 
boosting RMSE 824 : 0.1369009 

forest 824 : -0.400314 
forest mean 824 : -0.3927846 
forest RMSE 824 : 0.0512182 

nnet 824 : -0.2592337 
nnet mean 824 : -0.4162706 
nnet RMSE 824 : 0.1521831 


s: 825 
logit 825 : -0.4445814 
logit mean 825 : -0.4394661 
logit RMSE 825 : 0.07057871 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 825 : -0.1891798 
boosting mean 825 : -0.483264 
boosting RMSE 825 : 0.1370146 

forest 825 : -0.3750341 
forest mean 825 : -0.3927631 
forest RMSE 825 : 0.05119453 

nnet 825 : -0.1660193 
nnet mean 825 : -0.4159673 
nnet RMSE 825 : 0.1523088 


s: 826 
logit 826 : -0.4445885 
logit mean 826 : -0.4394723 
logit RMSE 826 : 0.07055304 

boosting 826 : -0.5584584 
boosting mean 826 : -0.483355 
boosting RMSE 826 : 0.1370426 

forest 826 : -0.4041809 
forest mean 826 : -0.3927769 
forest RMSE 826 : 0.05116373 

nnet 826 : -0.4716869 
nnet mean 826 : -0.4160347 
nnet RMSE 826 : 0.1522370 


s: 827 
logit 827 : -0.4767488 
logit mean 827 : -0.4395173 
logit RMSE 827 : 0.07056086 

boosting 827 : -0.5690398 
boosting mean 827 : -0.4834586 
boosting RMSE 827 : 0.1370858 

forest 827 : -0.4303984 
forest mean 827 : -0.3928224 
forest RMSE 827 : 0.05114372 

nnet 827 : -0.4800479 
nnet mean 827 : -0.4161121 
nnet RMSE 827 : 0.1521704 


s: 828 
logit 828 : -0.4434218 
logit mean 828 : -0.4395220 
logit RMSE 828 : 0.07053438 

boosting 828 : -0.4779124 
boosting mean 828 : -0.4834519 
boosting RMSE 828 : 0.1370297 

forest 828 : -0.3803601 
forest mean 828 : -0.3928074 
forest RMSE 828 : 0.05111738 

nnet 828 : -0.3419029 
nnet mean 828 : -0.4160225 
nnet RMSE 828 : 0.1520919 


s: 829 
logit 829 : -0.3984716 
logit mean 829 : -0.4394725 
logit RMSE 829 : 0.07049185 

boosting 829 : -0.475498 
boosting mean 829 : -0.4834423 
boosting RMSE 829 : 0.1369722 

forest 829 : -0.2907235 
forest mean 829 : -0.3926842 
forest RMSE 829 : 0.05122733 

nnet 829 : 0.008099848 
nnet mean 829 : -0.4155109 
nnet RMSE 829 : 0.1526596 


s: 830 
logit 830 : -0.3862779 
logit mean 830 : -0.4394084 
logit RMSE 830 : 0.07045098 

boosting 830 : -0.5227315 
boosting mean 830 : -0.4834896 
boosting RMSE 830 : 0.1369559 

forest 830 : -0.444768 
forest mean 830 : -0.392747 
forest RMSE 830 : 0.05122004 

nnet 830 : -0.5186175 
nnet mean 830 : -0.4156351 
nnet RMSE 830 : 0.1526231 


s: 831 
logit 831 : -0.467879 
logit mean 831 : -0.4394427 
logit RMSE 831 : 0.07044794 

boosting 831 : -0.6110306 
boosting mean 831 : -0.4836431 
boosting RMSE 831 : 0.1370691 

forest 831 : -0.3938455 
forest mean 831 : -0.3927483 
forest RMSE 831 : 0.05118965 

nnet 831 : -0.3975835 
nnet mean 831 : -0.4156134 
nnet RMSE 831 : 0.1525313 


s: 832 
logit 832 : -0.4845741 
logit mean 832 : -0.4394969 
logit RMSE 832 : 0.07046662 

boosting 832 : -0.615358 
boosting mean 832 : -0.4838014 
boosting RMSE 832 : 0.1371900 

forest 832 : -0.4952245 
forest mean 832 : -0.3928715 
forest RMSE 832 : 0.05126529 

nnet 832 : -0.4192821 
nnet mean 832 : -0.4156178 
nnet RMSE 832 : 0.1524411 


s: 833 
logit 833 : -0.46669 
logit mean 833 : -0.4395296 
logit RMSE 833 : 0.0704622 

boosting 833 : -0.4852076 
boosting mean 833 : -0.4838031 
boosting RMSE 833 : 0.1371394 

forest 833 : -0.3308517 
forest mean 833 : -0.392797 
forest RMSE 833 : 0.05129049 

nnet 833 : -0.4806584 
nnet mean 833 : -0.4156959 
nnet RMSE 833 : 0.1523752 


s: 834 
logit 834 : -0.4808511 
logit mean 834 : -0.4395791 
logit RMSE 834 : 0.07047558 

boosting 834 : -0.5444647 
boosting mean 834 : -0.4838759 
boosting RMSE 834 : 0.1371484 

forest 834 : -0.3532406 
forest mean 834 : -0.3927496 
forest RMSE 834 : 0.0512853 

nnet 834 : -0.495928 
nnet mean 834 : -0.4157921 
nnet RMSE 834 : 0.15232 


s: 835 
logit 835 : -0.3072315 
logit mean 835 : -0.4394206 
logit RMSE 835 : 0.07050649 

boosting 835 : -0.4787916 
boosting mean 835 : -0.4838698 
boosting RMSE 835 : 0.1370934 

forest 835 : -0.3598283 
forest mean 835 : -0.3927102 
forest RMSE 835 : 0.05127343 

nnet 835 : -0.4218276 
nnet mean 835 : -0.4157993 
nnet RMSE 835 : 0.1522306 


s: 836 
logit 836 : -0.4582829 
logit mean 836 : -0.4394432 
logit RMSE 836 : 0.07049314 

boosting 836 : -0.3101105 
boosting mean 836 : -0.4836619 
boosting RMSE 836 : 0.1370467 

forest 836 : -0.3749160 
forest mean 836 : -0.3926889 
forest RMSE 836 : 0.0512501 

nnet 836 : -0.4965148 
nnet mean 836 : -0.4158959 
nnet RMSE 836 : 0.1521762 


s: 837 
logit 837 : -0.4803289 
logit mean 837 : -0.4394920 
logit RMSE 837 : 0.07050571 

boosting 837 : -0.5804005 
boosting mean 837 : -0.4837775 
boosting RMSE 837 : 0.1371066 

forest 837 : -0.3795411 
forest mean 837 : -0.3926732 
forest RMSE 837 : 0.05122436 

nnet 837 : -0.08752761 
nnet mean 837 : -0.4155035 
nnet RMSE 837 : 0.1524683 


s: 838 
logit 838 : -0.4713272 
logit mean 838 : -0.43953 
logit RMSE 838 : 0.0705067 

boosting 838 : -0.5528675 
boosting mean 838 : -0.4838599 
boosting RMSE 838 : 0.1371265 

forest 838 : -0.4004917 
forest mean 838 : -0.3926825 
forest RMSE 838 : 0.05119379 

nnet 838 : -0.4240957 
nnet mean 838 : -0.4155138 
nnet RMSE 838 : 0.1523796 


s: 839 
logit 839 : -0.4230695 
logit mean 839 : -0.4395104 
logit RMSE 839 : 0.07046916 

boosting 839 : -0.5407766 
boosting mean 839 : -0.4839278 
boosting RMSE 839 : 0.1371309 

forest 839 : -0.3787943 
forest mean 839 : -0.392666 
forest RMSE 839 : 0.05116851 

nnet 839 : -0.3932441 
nnet mean 839 : -0.4154872 
nnet RMSE 839 : 0.1522889 


s: 840 
logit 840 : -0.3649548 
logit mean 840 : -0.4394217 
logit RMSE 840 : 0.07043758 

boosting 840 : -0.5815761 
boosting mean 840 : -0.484044 
boosting RMSE 840 : 0.1371924 

forest 840 : -0.5017377 
forest mean 840 : -0.3927958 
forest RMSE 840 : 0.05125838 

nnet 840 : -0.3027218 
nnet mean 840 : -0.415353 
nnet RMSE 840 : 0.1522352 


s: 841 
logit 841 : -0.4452457 
logit mean 841 : -0.4394286 
logit RMSE 841 : 0.07041298 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 841 : -0.5080686 
boosting mean 841 : -0.4840726 
boosting RMSE 841 : 0.1371614 

forest 841 : -0.4176079 
forest mean 841 : -0.3928253 
forest RMSE 841 : 0.05123149 

nnet 841 : -0.3399538 
nnet mean 841 : -0.4152633 
nnet RMSE 841 : 0.1521588 


s: 842 
logit 842 : -0.3787549 
logit mean 842 : -0.4393565 
logit RMSE 842 : 0.07037497 

boosting 842 : -0.4026281 
boosting mean 842 : -0.4839759 
boosting RMSE 842 : 0.13708 

forest 842 : -0.3664402 
forest mean 842 : -0.392794 
forest RMSE 842 : 0.05121412 

nnet 842 : -0.3662184 
nnet mean 842 : -0.4152051 
nnet RMSE 842 : 0.1520729 


s: 843 
logit 843 : -0.4735791 
logit mean 843 : -0.4393971 
logit RMSE 843 : 0.07037885 

boosting 843 : -0.5986644 
boosting mean 843 : -0.4841119 
boosting RMSE 843 : 0.1371694 

forest 843 : -0.4807983 
forest mean 843 : -0.3928984 
forest RMSE 843 : 0.05125933 

nnet 843 : -0.3376665 
nnet mean 843 : -0.4151131 
nnet RMSE 843 : 0.1519978 


s: 844 
logit 844 : -0.34956 
logit mean 844 : -0.4392907 
logit RMSE 844 : 0.07035857 

boosting 844 : -0.4475002 
boosting mean 844 : -0.4840685 
boosting RMSE 844 : 0.1370979 

forest 844 : -0.3651527 
forest mean 844 : -0.3928655 
forest RMSE 844 : 0.051243 

nnet 844 : -0.3536275 
nnet mean 844 : -0.4150403 
nnet RMSE 844 : 0.1519161 


s: 845 
logit 845 : -0.3749475 
logit mean 845 : -0.4392145 
logit RMSE 845 : 0.07032221 

boosting 845 : -0.5193066 
boosting mean 845 : -0.4841102 
boosting RMSE 845 : 0.1370782 

forest 845 : -0.3780831 
forest mean 845 : -0.392848 
forest RMSE 845 : 0.05121822 

nnet 845 : -0.5356397 
nnet mean 845 : -0.415183 
nnet RMSE 845 : 0.1518979 


s: 846 
logit 846 : -0.3785669 
logit mean 846 : -0.4391428 
logit RMSE 846 : 0.0702845 

boosting 846 : -0.5379579 
boosting mean 846 : -0.4841739 
boosting RMSE 846 : 0.1370793 

forest 846 : -0.5095031 
forest mean 846 : -0.3929859 
forest RMSE 846 : 0.0513262 

nnet 846 : -0.7963398 
nnet mean 846 : -0.4156335 
nnet RMSE 846 : 0.1524184 


s: 847 
logit 847 : -0.2935712 
logit mean 847 : -0.438971 
logit RMSE 847 : 0.07033812 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 847 : -0.3156595 
boosting mean 847 : -0.4839749 
boosting RMSE 847 : 0.1370290 

forest 847 : -0.3440806 
forest mean 847 : -0.3929282 
forest RMSE 847 : 0.05133186 

nnet 847 : -0.1423039 
nnet mean 847 : -0.4153108 
nnet RMSE 847 : 0.1525855 


s: 848 
logit 848 : -0.4132953 
logit mean 848 : -0.4389407 
logit RMSE 848 : 0.07029812 

boosting 848 : -0.4050397 
boosting mean 848 : -0.4838819 
boosting RMSE 848 : 0.1369482 

forest 848 : -0.3956635 
forest mean 848 : -0.3929314 
forest RMSE 848 : 0.0513018 

nnet 848 : -0.438296 
nnet mean 848 : -0.4153379 
nnet RMSE 848 : 0.1525012 


s: 849 
logit 849 : -0.4097508 
logit mean 849 : -0.4389063 
logit RMSE 849 : 0.0702575 

boosting 849 : -0.6171205 
boosting mean 849 : -0.4840388 
boosting RMSE 849 : 0.1370703 

forest 849 : -0.4063188 
forest mean 849 : -0.3929471 
forest RMSE 849 : 0.05127204 

nnet 849 : -0.4037327 
nnet mean 849 : -0.4153243 
nnet RMSE 849 : 0.1524114 


s: 850 
logit 850 : -0.4521811 
logit mean 850 : -0.4389219 
logit RMSE 850 : 0.07023897 

boosting 850 : -0.5328418 
boosting mean 850 : -0.4840962 
boosting RMSE 850 : 0.1370654 

forest 850 : -0.3185283 
forest mean 850 : -0.3928596 
forest RMSE 850 : 0.05131801 

nnet 850 : -0.1802112 
nnet mean 850 : -0.4150477 
nnet RMSE 850 : 0.1525082 


s: 851 
logit 851 : -0.4822918 
logit mean 851 : -0.4389729 
logit RMSE 851 : 0.07025435 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 851 : -0.398347 
boosting mean 851 : -0.4839954 
boosting RMSE 851 : 0.1369848 

forest 851 : -0.3262812 
forest mean 851 : -0.3927814 
forest RMSE 851 : 0.05135007 

nnet 851 : -0.09086035 
nnet mean 851 : -0.4146667 
nnet RMSE 851 : 0.1527865 


s: 852 
logit 852 : -0.4524696 
logit mean 852 : -0.4389887 
logit RMSE 852 : 0.07023611 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 852 : -0.5108162 
boosting mean 852 : -0.4840269 
boosting RMSE 852 : 0.1369570 

forest 852 : -0.4516359 
forest mean 852 : -0.3928504 
forest RMSE 852 : 0.05135041 

nnet 852 : -0.6541732 
nnet mean 852 : -0.4149478 
nnet RMSE 852 : 0.1529449 


s: 853 
logit 853 : -0.4612569 
logit mean 853 : -0.4390148 
logit RMSE 853 : 0.07022626 

boosting 853 : -0.5365691 
boosting mean 853 : -0.4840885 
boosting RMSE 853 : 0.1369566 

forest 853 : -0.4129276 
forest mean 853 : -0.392874 
forest RMSE 853 : 0.05132221 

nnet 853 : -0.2755095 
nnet mean 853 : -0.4147844 
nnet RMSE 853 : 0.1529147 


s: 854 
logit 854 : -0.5462246 
logit mean 854 : -0.4391404 
logit RMSE 854 : 0.07036327 

boosting 854 : -0.5433948 
boosting mean 854 : -0.484158 
boosting RMSE 854 : 0.1369643 

forest 854 : -0.3448597 
forest mean 854 : -0.3928178 
forest RMSE 854 : 0.05132684 

nnet 854 : -0.3100259 
nnet mean 854 : -0.4146617 
nnet RMSE 854 : 0.1528561 


s: 855 
logit 855 : -0.4576789 
logit mean 855 : -0.4391621 
logit RMSE 855 : 0.07034977 

boosting 855 : -0.41119 
boosting mean 855 : -0.4840726 
boosting RMSE 855 : 0.1368847 

forest 855 : -0.3334979 
forest mean 855 : -0.3927484 
forest RMSE 855 : 0.05134721 

nnet 855 : -0.4469868 
nnet mean 855 : -0.4146995 
nnet RMSE 855 : 0.1527751 


s: 856 
logit 856 : -0.5401819 
logit mean 856 : -0.4392801 
logit RMSE 856 : 0.07047173 

boosting 856 : -0.3243934 
boosting mean 856 : -0.4838861 
boosting RMSE 856 : 0.1368291 

forest 856 : -0.4400016 
forest mean 856 : -0.3928036 
forest RMSE 856 : 0.05133542 

nnet 856 : -0.1877529 
nnet mean 856 : -0.4144344 
nnet RMSE 856 : 0.1528581 


s: 857 
logit 857 : -0.411939 
logit mean 857 : -0.4392482 
logit RMSE 857 : 0.07043179 

boosting 857 : -0.4760818 
boosting mean 857 : -0.483877 
boosting RMSE 857 : 0.1367740 

forest 857 : -0.3442831 
forest mean 857 : -0.3927470 
forest RMSE 857 : 0.05134075 

nnet 857 : -0.4944657 
nnet mean 857 : -0.4145278 
nnet RMSE 857 : 0.152803 


s: 858 
logit 858 : -0.4063447 
logit mean 858 : -0.4392098 
logit RMSE 858 : 0.07039106 

boosting 858 : -0.3960678 
boosting mean 858 : -0.4837746 
boosting RMSE 858 : 0.1366943 

forest 858 : -0.4155946 
forest mean 858 : -0.3927736 
forest RMSE 858 : 0.05131358 

nnet 858 : -0.4341673 
nnet mean 858 : -0.4145506 
nnet RMSE 858 : 0.1527184 


s: 859 
logit 859 : -0.4429317 
logit mean 859 : -0.4392142 
logit RMSE 859 : 0.07036533 

boosting 859 : -0.5004315 
boosting mean 859 : -0.483794 
boosting RMSE 859 : 0.1366577 

forest 859 : -0.4223569 
forest mean 859 : -0.392808 
forest RMSE 859 : 0.05128938 

nnet 859 : -0.6041438 
nnet mean 859 : -0.4147714 
nnet RMSE 859 : 0.1527883 


s: 860 
logit 860 : -0.3410560 
logit mean 860 : -0.4391 
logit RMSE 860 : 0.07035312 

boosting 860 : -0.6362551 
boosting mean 860 : -0.4839713 
boosting RMSE 860 : 0.1368156 

forest 860 : -0.3644032 
forest mean 860 : -0.392775 
forest RMSE 860 : 0.05127392 

nnet 860 : -0.6936621 
nnet mean 860 : -0.4150957 
nnet RMSE 860 : 0.1530274 


s: 861 
logit 861 : -0.4524435 
logit mean 861 : -0.4391155 
logit RMSE 861 : 0.07033497 

boosting 861 : -0.4422121 
boosting mean 861 : -0.4839228 
boosting RMSE 861 : 0.1367437 

forest 861 : -0.3949485 
forest mean 861 : -0.3927775 
forest RMSE 861 : 0.05124443 

nnet 861 : -0.2892338 
nnet mean 861 : -0.4149495 
nnet RMSE 861 : 0.1529851 


s: 862 
logit 862 : -0.4926836 
logit mean 862 : -0.4391777 
logit RMSE 862 : 0.07036501 

boosting 862 : -0.5009916 
boosting mean 862 : -0.4839426 
boosting RMSE 862 : 0.1367077 

forest 862 : -0.3543327 
forest mean 862 : -0.3927329 
forest RMSE 862 : 0.05123831 

nnet 862 : -0.1809874 
nnet mean 862 : -0.4146781 
nnet RMSE 862 : 0.1530782 


s: 863 
logit 863 : -0.4055463 
logit mean 863 : -0.4391387 
logit RMSE 863 : 0.07032448 

boosting 863 : -0.55622 
boosting mean 863 : -0.4840264 
boosting RMSE 863 : 0.1367319 

forest 863 : -0.4435775 
forest mean 863 : -0.3927918 
forest RMSE 863 : 0.05123009 

nnet 863 : -0.4135122 
nnet mean 863 : -0.4146767 
nnet RMSE 863 : 0.1529902 


s: 864 
logit 864 : -0.4002294 
logit mean 864 : -0.4390937 
logit RMSE 864 : 0.07028377 

boosting 864 : -0.4965979 
boosting mean 864 : -0.4840409 
boosting RMSE 864 : 0.1366922 

forest 864 : -0.366762 
forest mean 864 : -0.3927617 
forest RMSE 864 : 0.05121292 

nnet 864 : -0.5314108 
nnet mean 864 : -0.4148118 
nnet RMSE 864 : 0.1529670 


s: 865 
logit 865 : -0.4844278 
logit mean 865 : -0.4391461 
logit RMSE 865 : 0.07030177 

boosting 865 : -0.4348495 
boosting mean 865 : -0.483984 
boosting RMSE 865 : 0.1366183 

forest 865 : -0.4506056 
forest mean 865 : -0.3928286 
forest RMSE 865 : 0.05121223 

nnet 865 : -0.03481793 
nnet mean 865 : -0.4143725 
nnet RMSE 865 : 0.1533819 


s: 866 
logit 866 : -0.3507284 
logit mean 866 : -0.439044 
logit RMSE 866 : 0.07028111 

boosting 866 : -0.5215616 
boosting mean 866 : -0.4840274 
boosting RMSE 866 : 0.1366019 

forest 866 : -0.3555029 
forest mean 866 : -0.3927855 
forest RMSE 866 : 0.05120498 

nnet 866 : -0.3183352 
nnet mean 866 : -0.4142616 
nnet RMSE 866 : 0.1533185 


s: 867 
logit 867 : -0.3963767 
logit mean 867 : -0.4389948 
logit RMSE 867 : 0.07024068 

boosting 867 : -0.3869277 
boosting mean 867 : -0.4839154 
boosting RMSE 867 : 0.1365238 

forest 867 : -0.4255827 
forest mean 867 : -0.3928233 
forest RMSE 867 : 0.05118282 

nnet 867 : -0.3661307 
nnet mean 867 : -0.4142061 
nnet RMSE 867 : 0.1532343 


s: 868 
logit 868 : -0.4785139 
logit mean 868 : -0.4390403 
logit RMSE 868 : 0.07025077 

boosting 868 : -0.5047415 
boosting mean 868 : -0.4839394 
boosting RMSE 868 : 0.1364915 

forest 868 : -0.4680116 
forest mean 868 : -0.3929099 
forest RMSE 868 : 0.05120539 

nnet 868 : -0.2673704 
nnet mean 868 : -0.4140369 
nnet RMSE 868 : 0.1532122 


s: 869 
logit 869 : -0.4807097 
logit mean 869 : -0.4390882 
logit RMSE 869 : 0.0702637 

boosting 869 : -0.4706171 
boosting mean 869 : -0.4839241 
boosting RMSE 869 : 0.1364340 

forest 869 : -0.4182005 
forest mean 869 : -0.392939 
forest RMSE 869 : 0.05117964 

nnet 869 : -0.6626644 
nnet mean 869 : -0.414323 
nnet RMSE 869 : 0.1533830 


s: 870 
logit 870 : -0.3818630 
logit mean 870 : -0.4390225 
logit RMSE 870 : 0.070226 

boosting 870 : -0.4939798 
boosting mean 870 : -0.4839357 
boosting RMSE 870 : 0.1363927 

forest 870 : -0.3463477 
forest mean 870 : -0.3928855 
forest RMSE 870 : 0.05118255 

nnet 870 : -0.3685812 
nnet mean 870 : -0.4142705 
nnet RMSE 870 : 0.1532986 


s: 871 
logit 871 : -0.4082961 
logit mean 871 : -0.4389872 
logit RMSE 871 : 0.07018624 

boosting 871 : -0.4455219 
boosting mean 871 : -0.4838916 
boosting RMSE 871 : 0.1363231 

forest 871 : -0.3809726 
forest mean 871 : -0.3928718 
forest RMSE 871 : 0.05115722 

nnet 871 : -0.3250956 
nnet mean 871 : -0.4141681 
nnet RMSE 871 : 0.1532316 


s: 872 
logit 872 : -0.3851185 
logit mean 872 : -0.4389254 
logit RMSE 872 : 0.07014779 

boosting 872 : -0.3710455 
boosting mean 872 : -0.4837621 
boosting RMSE 872 : 0.1362485 

forest 872 : -0.3864312 
forest mean 872 : -0.3928644 
forest RMSE 872 : 0.05112995 

nnet 872 : -0.3051492 
nnet mean 872 : -0.4140431 
nnet RMSE 872 : 0.1531774 


s: 873 
logit 873 : -0.4792655 
logit mean 873 : -0.4389716 
logit RMSE 873 : 0.07015891 

boosting 873 : -0.3666263 
boosting mean 873 : -0.483628 
boosting RMSE 873 : 0.1361751 

forest 873 : -0.3890852 
forest mean 873 : -0.3928601 
forest RMSE 873 : 0.05110199 

nnet 873 : -0.6118409 
nnet mean 873 : -0.4142696 
nnet RMSE 873 : 0.1532574 


s: 874 
logit 874 : -0.5086132 
logit mean 874 : -0.4390513 
logit RMSE 874 : 0.07021495 

boosting 874 : -0.4252022 
boosting mean 874 : -0.4835611 
boosting RMSE 874 : 0.1360999 

forest 874 : -0.3973401 
forest mean 874 : -0.3928652 
forest RMSE 874 : 0.05107283 

nnet 874 : -0.2885014 
nnet mean 874 : -0.4141257 
nnet RMSE 874 : 0.1532161 


s: 875 
logit 875 : -0.4491084 
logit mean 875 : -0.4390628 
logit RMSE 875 : 0.07019445 

boosting 875 : -0.5061205 
boosting mean 875 : -0.4835869 
boosting RMSE 875 : 0.1360694 

forest 875 : -0.4447872 
forest mean 875 : -0.3929246 
forest RMSE 875 : 0.05106608 

nnet 875 : -0.4573117 
nnet mean 875 : -0.4141751 
nnet RMSE 875 : 0.1531408 


s: 876 
logit 876 : -0.2835658 
logit mean 876 : -0.4388853 
logit RMSE 876 : 0.07026458 

boosting 876 : -0.4646846 
boosting mean 876 : -0.4835653 
boosting RMSE 876 : 0.1360092 

forest 876 : -0.367667 
forest mean 876 : -0.3928957 
forest RMSE 876 : 0.05104862 

nnet 876 : -0.4454734 
nnet mean 876 : -0.4142108 
nnet RMSE 876 : 0.1530611 


s: 877 
logit 877 : -0.4052928 
logit mean 877 : -0.438847 
logit RMSE 877 : 0.07022474 

boosting 877 : -0.3133582 
boosting mean 877 : -0.4833712 
boosting RMSE 877 : 0.1359632 

forest 877 : -0.3704597 
forest mean 877 : -0.3928701 
forest RMSE 877 : 0.05102926 

nnet 877 : -0.5874204 
nnet mean 877 : -0.4144083 
nnet RMSE 877 : 0.1531047 


s: 878 
logit 878 : -0.4453985 
logit mean 878 : -0.4388544 
logit RMSE 878 : 0.07020146 

boosting 878 : -0.8197352 
boosting mean 878 : -0.4837543 
boosting RMSE 878 : 0.1366220 

forest 878 : -0.3827290 
forest mean 878 : -0.3928586 
forest RMSE 878 : 0.05100352 

nnet 878 : -0.5886151 
nnet mean 878 : -0.4146067 
nnet RMSE 878 : 0.1531498 


s: 879 
logit 879 : -0.4661367 
logit mean 879 : -0.4388855 
logit RMSE 879 : 0.07019697 

boosting 879 : -0.3255165 
boosting mean 879 : -0.4835743 
boosting RMSE 879 : 0.1365674 

forest 879 : -0.2971495 
forest mean 879 : -0.3927497 
forest RMSE 879 : 0.05109241 

nnet 879 : -0.6565264 
nnet mean 879 : -0.4148820 
nnet RMSE 879 : 0.153307 


s: 880 
logit 880 : -0.5875109 
logit mean 880 : -0.4390544 
logit RMSE 880 : 0.07044125 

boosting 880 : -0.3891092 
boosting mean 880 : -0.483467 
boosting RMSE 880 : 0.1364903 

forest 880 : -0.4020993 
forest mean 880 : -0.3927603 
forest RMSE 880 : 0.05106342 

nnet 880 : -0.4880307 
nnet mean 880 : -0.4149651 
nnet RMSE 880 : 0.1532486 


s: 881 
logit 881 : -0.5809913 
logit mean 881 : -0.4392155 
logit RMSE 881 : 0.07066484 

boosting 881 : -0.3808918 
boosting mean 881 : -0.4833505 
boosting RMSE 881 : 0.1364143 

forest 881 : -0.313216 
forest mean 881 : -0.3926700 
forest RMSE 881 : 0.05111811 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 881 : -0.2288760 
nnet mean 881 : -0.4147539 
nnet RMSE 881 : 0.1532701 


s: 882 
logit 882 : -0.5039193 
logit mean 882 : -0.4392888 
logit RMSE 882 : 0.0707114 

boosting 882 : -0.531418 
boosting mean 882 : -0.483405 
boosting RMSE 882 : 0.1364088 

forest 882 : -0.3496517 
forest mean 882 : -0.3926213 
forest RMSE 882 : 0.05111725 

nnet 882 : -0.4616934 
nnet mean 882 : -0.4148071 
nnet RMSE 882 : 0.1531972 


s: 883 
logit 883 : -0.3850131 
logit mean 883 : -0.4392274 
logit RMSE 883 : 0.07067315 

boosting 883 : -0.4415625 
boosting mean 883 : -0.4833577 
boosting RMSE 883 : 0.1363387 

forest 883 : -0.3593007 
forest mean 883 : -0.3925835 
forest RMSE 883 : 0.05110665 

nnet 883 : -0.2623400 
nnet mean 883 : -0.4146344 
nnet RMSE 883 : 0.1531805 


s: 884 
logit 884 : -0.3902953 
logit mean 884 : -0.439172 
logit RMSE 884 : 0.07063392 

boosting 884 : -0.7000364 
boosting mean 884 : -0.4836028 
boosting RMSE 884 : 0.1366347 

forest 884 : -0.3908484 
forest mean 884 : -0.3925816 
forest RMSE 884 : 0.05107866 

nnet 884 : -0.2851545 
nnet mean 884 : -0.4144879 
nnet RMSE 884 : 0.1531426 


s: 885 
logit 885 : -0.4032573 
logit mean 885 : -0.4391314 
logit RMSE 885 : 0.07059409 

boosting 885 : -0.474381 
boosting mean 885 : -0.4835924 
boosting RMSE 885 : 0.1365804 

forest 885 : -0.2862668 
forest mean 885 : -0.3924614 
forest RMSE 885 : 0.05119275 

nnet 885 : -0.358009 
nnet mean 885 : -0.4144241 
nnet RMSE 885 : 0.1530626 


s: 886 
logit 886 : -0.4418962 
logit mean 886 : -0.4391346 
logit RMSE 886 : 0.07056827 

boosting 886 : -0.4026560 
boosting mean 886 : -0.483501 
boosting RMSE 886 : 0.1365033 

forest 886 : -0.4653008 
forest mean 886 : -0.3925437 
forest RMSE 886 : 0.05121087 

nnet 886 : -0.3274302 
nnet mean 886 : -0.4143259 
nnet RMSE 886 : 0.1529956 


s: 887 
logit 887 : -0.3163312 
logit mean 887 : -0.4389961 
logit RMSE 887 : 0.07058441 

boosting 887 : -0.2322059 
boosting mean 887 : -0.4832177 
boosting RMSE 887 : 0.1365426 

forest 887 : -0.3479993 
forest mean 887 : -0.3924934 
forest RMSE 887 : 0.05121176 

nnet 887 : -0.5409142 
nnet mean 887 : -0.4144686 
nnet RMSE 887 : 0.1529825 


s: 888 
logit 888 : -0.3280073 
logit mean 888 : -0.4388711 
logit RMSE 888 : 0.07058601 

boosting 888 : -0.3342588 
boosting mean 888 : -0.4830499 
boosting RMSE 888 : 0.1364835 

forest 888 : -0.4503744 
forest mean 888 : -0.3925586 
forest RMSE 888 : 0.05121083 

nnet 888 : -0.3626001 
nnet mean 888 : -0.4144102 
nnet RMSE 888 : 0.1529015 


s: 889 
logit 889 : -0.4539758 
logit mean 889 : -0.4388881 
logit RMSE 889 : 0.07056953 

boosting 889 : -0.4278945 
boosting mean 889 : -0.4829879 
boosting RMSE 889 : 0.1364100 

forest 889 : -0.4142740 
forest mean 889 : -0.3925830 
forest RMSE 889 : 0.05118426 

nnet 889 : -0.4112077 
nnet mean 889 : -0.4144066 
nnet RMSE 889 : 0.1528159 


s: 890 
logit 890 : -0.448521 
logit mean 890 : -0.4388989 
logit RMSE 890 : 0.07054862 

boosting 890 : -0.5398935 
boosting mean 890 : -0.4830518 
boosting RMSE 890 : 0.1364139 

forest 890 : -0.4063227 
forest mean 890 : -0.3925985 
forest RMSE 890 : 0.05115593 

nnet 890 : -0.3162203 
nnet mean 890 : -0.4142963 
nnet RMSE 890 : 0.1527559 


s: 891 
logit 891 : -0.4368837 
logit mean 891 : -0.4388967 
logit RMSE 891 : 0.07051985 

boosting 891 : -0.5247223 
boosting mean 891 : -0.4830986 
boosting RMSE 891 : 0.1364014 

forest 891 : -0.411418 
forest mean 891 : -0.3926196 
forest RMSE 891 : 0.05112865 

nnet 891 : -0.3512412 
nnet mean 891 : -0.4142255 
nnet RMSE 891 : 0.1526789 


s: 892 
logit 892 : -0.5085136 
logit mean 892 : -0.4389747 
logit RMSE 892 : 0.0705739 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 892 : -0.5209546 
boosting mean 892 : -0.4831411 
boosting RMSE 892 : 0.1363850 

forest 892 : -0.4713971 
forest mean 892 : -0.3927079 
forest RMSE 892 : 0.05115587 

nnet 892 : -0.3598008 
nnet mean 892 : -0.4141645 
nnet RMSE 892 : 0.1525992 


s: 893 
logit 893 : -0.5337241 
logit mean 893 : -0.4390808 
logit RMSE 893 : 0.07067618 

boosting 893 : -0.6165404 
boosting mean 893 : -0.4832904 
boosting RMSE 893 : 0.1365011 

forest 893 : -0.3768788 
forest mean 893 : -0.3926902 
forest RMSE 893 : 0.05113307 

nnet 893 : -0.3743537 
nnet mean 893 : -0.4141199 
nnet RMSE 893 : 0.1525161 


s: 894 
logit 894 : -0.4731689 
logit mean 894 : -0.4391190 
logit RMSE 894 : 0.07067901 

boosting 894 : -0.5029949 
boosting mean 894 : -0.4833125 
boosting RMSE 894 : 0.1364682 

forest 894 : -0.4220367 
forest mean 894 : -0.392723 
forest RMSE 894 : 0.05110978 

nnet 894 : -0.4276062 
nnet mean 894 : -0.414135 
nnet RMSE 894 : 0.1524336 


s: 895 
logit 895 : -0.4295130 
logit mean 895 : -0.4391082 
logit RMSE 895 : 0.0706464 

boosting 895 : -0.4260646 
boosting mean 895 : -0.4832485 
boosting RMSE 895 : 0.1363948 

forest 895 : -0.4165164 
forest mean 895 : -0.3927496 
forest RMSE 895 : 0.0510842 

nnet 895 : -0.4269553 
nnet mean 895 : -0.4141494 
nnet RMSE 895 : 0.1523511 


s: 896 
logit 896 : -0.3890164 
logit mean 896 : -0.4390523 
logit RMSE 896 : 0.07060792 

boosting 896 : -0.4384161 
boosting mean 896 : -0.4831985 
boosting RMSE 896 : 0.1363247 

forest 896 : -0.3867588 
forest mean 896 : -0.3927429 
forest RMSE 896 : 0.0510576 

nnet 896 : -0.4255848 
nnet mean 896 : -0.4141621 
nnet RMSE 896 : 0.1522685 


s: 897 
logit 897 : -0.4817691 
logit mean 897 : -0.4390999 
logit RMSE 897 : 0.07062135 

boosting 897 : -0.5571069 
boosting mean 897 : -0.4832809 
boosting RMSE 897 : 0.1363496 

forest 897 : -0.4470815 
forest mean 897 : -0.3928035 
forest RMSE 897 : 0.05105334 

nnet 897 : -0.436848 
nnet mean 897 : -0.4141874 
nnet RMSE 897 : 0.1521885 


s: 898 
logit 898 : -0.4341681 
logit mean 898 : -0.4390944 
logit RMSE 898 : 0.07059123 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 898 : -0.6469352 
boosting mean 898 : -0.4834631 
boosting RMSE 898 : 0.1365226 

forest 898 : -0.4302266 
forest mean 898 : -0.3928452 
forest RMSE 898 : 0.05103488 

nnet 898 : -0.5375905 
nnet mean 898 : -0.4143248 
nnet RMSE 898 : 0.1521731 


s: 899 
logit 899 : -0.4701085 
logit mean 899 : -0.4391289 
logit RMSE 899 : 0.07059069 

boosting 899 : -0.5313018 
boosting mean 899 : -0.4835163 
boosting RMSE 899 : 0.1365169 

forest 899 : -0.350414 
forest mean 899 : -0.392798 
forest RMSE 899 : 0.05103329 

nnet 899 : -0.6382255 
nnet mean 899 : -0.4145739 
nnet RMSE 899 : 0.1522958 


s: 900 
logit 900 : -0.5444659 
logit mean 900 : -0.439246 
logit RMSE 900 : 0.07071561 

boosting 900 : -0.5736866 
boosting mean 900 : -0.4836165 
boosting RMSE 900 : 0.1365638 

forest 900 : -0.5080996 
forest mean 900 : -0.3929261 
forest RMSE 900 : 0.05113205 

nnet 900 : -0.7224082 
nnet mean 900 : -0.4149159 
nnet RMSE 900 : 0.1525901 


s: 901 
logit 901 : -0.4282184 
logit mean 901 : -0.4392337 
logit RMSE 901 : 0.07068261 

boosting 901 : -0.3777282 
boosting mean 901 : -0.483499 
boosting RMSE 901 : 0.13649 

forest 901 : -0.3756835 
forest mean 901 : -0.3929069 
forest RMSE 901 : 0.05111009 

nnet 901 : -0.6069966 
nnet mean 901 : -0.4151291 
nnet RMSE 901 : 0.1526612 


s: 902 
logit 902 : -0.3917968 
logit mean 902 : -0.4391812 
logit RMSE 902 : 0.07064395 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 902 : -0.6363262 
boosting mean 902 : -0.4836684 
boosting RMSE 902 : 0.1366411 

forest 902 : -0.4195817 
forest mean 902 : -0.3929365 
forest RMSE 902 : 0.05108591 

nnet 902 : -0.4927614 
nnet mean 902 : -0.4152152 
nnet RMSE 902 : 0.1526078 


s: 903 
logit 903 : -0.3499741 
logit mean 903 : -0.4390824 
logit RMSE 903 : 0.07062445 

boosting 903 : -0.3354449 
boosting mean 903 : -0.4835043 
boosting RMSE 903 : 0.1365823 

forest 903 : -0.3524909 
forest mean 903 : -0.3928917 
forest RMSE 903 : 0.05108209 

nnet 903 : -0.2818863 
nnet mean 903 : -0.4150675 
nnet RMSE 903 : 0.1525739 


s: 904 
logit 904 : -0.3915935 
logit mean 904 : -0.4390298 
logit RMSE 904 : 0.07058593 

boosting 904 : -0.4395276 
boosting mean 904 : -0.4834556 
boosting RMSE 904 : 0.1365131 

forest 904 : -0.3724719 
forest mean 904 : -0.3928691 
forest RMSE 904 : 0.05106204 

nnet 904 : -0.4175496 
nnet mean 904 : -0.4150703 
nnet RMSE 904 : 0.1524906 


s: 905 
logit 905 : -0.4703425 
logit mean 905 : -0.4390644 
logit RMSE 905 : 0.07058566 

boosting 905 : -0.5269577 
boosting mean 905 : -0.4835037 
boosting RMSE 905 : 0.1365029 

forest 905 : -0.3510981 
forest mean 905 : -0.392823 
forest RMSE 905 : 0.0510597 

nnet 905 : -0.3572992 
nnet mean 905 : -0.4150064 
nnet RMSE 905 : 0.1524130 


s: 906 
logit 906 : -0.4498902 
logit mean 906 : -0.4390764 
logit RMSE 906 : 0.07056616 

boosting 906 : -0.3693270 
boosting mean 906 : -0.4833777 
boosting RMSE 906 : 0.1364313 

forest 906 : -0.4032181 
forest mean 906 : -0.3928345 
forest RMSE 906 : 0.05103163 

nnet 906 : -0.3726082 
nnet mean 906 : -0.4149596 
nnet RMSE 906 : 0.1523316 


s: 907 
logit 907 : -0.3412723 
logit mean 907 : -0.4389686 
logit RMSE 907 : 0.0705542 

boosting 907 : -0.3746504 
boosting mean 907 : -0.4832578 
boosting RMSE 907 : 0.1363587 

forest 907 : -0.402043 
forest mean 907 : -0.3928446 
forest RMSE 907 : 0.05100353 

nnet 907 : -0.55296 
nnet mean 907 : -0.4151118 
nnet RMSE 907 : 0.1523323 


s: 908 
logit 908 : -0.3616614 
logit mean 908 : -0.4388834 
logit RMSE 908 : 0.07052682 

boosting 908 : -0.3978773 
boosting mean 908 : -0.4831638 
boosting RMSE 908 : 0.1362836 

forest 908 : -0.4273805 
forest mean 908 : -0.3928826 
forest RMSE 908 : 0.05098353 

nnet 908 : -0.6691037 
nnet mean 908 : -0.4153915 
nnet RMSE 908 : 0.1525100 


s: 909 
logit 909 : -0.5040776 
logit mean 909 : -0.4389551 
logit RMSE 909 : 0.07057249 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 909 : -0.6194165 
boosting mean 909 : -0.4833137 
boosting RMSE 909 : 0.1364029 

forest 909 : -0.3997895 
forest mean 909 : -0.3928902 
forest RMSE 909 : 0.05095548 

nnet 909 : -0.6488864 
nnet mean 909 : -0.4156484 
nnet RMSE 909 : 0.1526495 


s: 910 
logit 910 : -0.4772647 
logit mean 910 : -0.4389972 
logit RMSE 910 : 0.0705802 

boosting 910 : -0.5053031 
boosting mean 910 : -0.4833378 
boosting RMSE 910 : 0.1363726 

forest 910 : -0.3951698 
forest mean 910 : -0.3928927 
forest RMSE 910 : 0.05092773 

nnet 910 : -0.4145327 
nnet mean 910 : -0.4156472 
nnet RMSE 910 : 0.1525664 


s: 911 
logit 911 : -0.4361825 
logit mean 911 : -0.4389941 
logit RMSE 911 : 0.07055163 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 911 : -0.4768314 
boosting mean 911 : -0.4833307 
boosting RMSE 911 : 0.1363215 

forest 911 : -0.4229128 
forest mean 911 : -0.3929257 
forest RMSE 911 : 0.05090543 

nnet 911 : -0.438288 
nnet mean 911 : -0.415672 
nnet RMSE 911 : 0.1524879 


s: 912 
logit 912 : -0.3736834 
logit mean 912 : -0.4389225 
logit RMSE 912 : 0.07051833 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 912 : -0.598671 
boosting mean 912 : -0.4834572 
boosting RMSE 912 : 0.1364055 

forest 912 : -0.438149 
forest mean 912 : -0.3929753 
forest RMSE 912 : 0.0508932 

nnet 912 : -0.3289049 
nnet mean 912 : -0.4155769 
nnet RMSE 912 : 0.1524224 


s: 913 
logit 913 : -0.4223292 
logit mean 913 : -0.4389044 
logit RMSE 913 : 0.07048357 

boosting 913 : -0.3409459 
boosting mean 913 : -0.4833011 
boosting RMSE 913 : 0.1363448 

forest 913 : -0.3742607 
forest mean 913 : -0.3929548 
forest RMSE 913 : 0.05087245 

nnet 913 : -0.2242242 
nnet mean 913 : -0.4153673 
nnet RMSE 913 : 0.1524500 


s: 914 
logit 914 : -0.3792770 
logit mean 914 : -0.4388391 
logit RMSE 914 : 0.07044834 

boosting 914 : -0.5358033 
boosting mean 914 : -0.4833585 
boosting RMSE 914 : 0.1363442 

forest 914 : -0.2978879 
forest mean 914 : -0.3928508 
forest RMSE 914 : 0.05095667 

nnet 914 : -0.3028463 
nnet mean 914 : -0.4152442 
nnet RMSE 914 : 0.1524004 


s: 915 
logit 915 : -0.4931508 
logit mean 915 : -0.4388985 
logit RMSE 915 : 0.07047714 

boosting 915 : -0.4411056 
boosting mean 915 : -0.4833123 
boosting RMSE 915 : 0.1362764 

forest 915 : -0.3466465 
forest mean 915 : -0.3928003 
forest RMSE 915 : 0.05095935 

nnet 915 : -0.1693033 
nnet mean 915 : -0.4149754 
nnet RMSE 915 : 0.1525080 


s: 916 
logit 916 : -0.4165068 
logit mean 916 : -0.438874 
logit RMSE 916 : 0.07044077 

boosting 916 : -0.3846632 
boosting mean 916 : -0.4832046 
boosting RMSE 916 : 0.1362030 

forest 916 : -0.3916468 
forest mean 916 : -0.392799 
forest RMSE 916 : 0.05093228 

nnet 916 : -0.5162633 
nnet mean 916 : -0.415086 
nnet RMSE 916 : 0.1524731 


s: 917 
logit 917 : -0.47463 
logit mean 917 : -0.438913 
logit RMSE 917 : 0.07044547 

boosting 917 : -0.4299574 
boosting mean 917 : -0.4831466 
boosting RMSE 917 : 0.1361323 

forest 917 : -0.3167060 
forest mean 917 : -0.3927160 
forest RMSE 917 : 0.05097876 

nnet 917 : -0.2578810 
nnet mean 917 : -0.4149145 
nnet RMSE 917 : 0.1524622 


s: 918 
logit 918 : -0.4171286 
logit mean 918 : -0.4388893 
logit RMSE 918 : 0.07040936 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 918 : -0.6150818 
boosting mean 918 : -0.4832903 
boosting RMSE 918 : 0.1362432 

forest 918 : -0.4257066 
forest mean 918 : -0.392752 
forest RMSE 918 : 0.05095805 

nnet 918 : -0.2131944 
nnet mean 918 : -0.4146948 
nnet RMSE 918 : 0.1525038 


s: 919 
logit 919 : -0.4024025 
logit mean 919 : -0.4388496 
logit RMSE 919 : 0.07037109 

boosting 919 : -0.5667611 
boosting mean 919 : -0.4833811 
boosting RMSE 919 : 0.1362801 

forest 919 : -0.467511 
forest mean 919 : -0.3928333 
forest RMSE 919 : 0.05097898 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 919 : -0.3785387 
nnet mean 919 : -0.4146554 
nnet RMSE 919 : 0.1524224 


s: 920 
logit 920 : -0.5048139 
logit mean 920 : -0.4389213 
logit RMSE 920 : 0.07041768 

boosting 920 : -0.5021667 
boosting mean 920 : -0.4834015 
boosting RMSE 920 : 0.1362477 

forest 920 : -0.541806 
forest mean 920 : -0.3929953 
forest RMSE 920 : 0.05116531 

nnet 920 : -0.4751585 
nnet mean 920 : -0.4147212 
nnet RMSE 920 : 0.1523597 


s: 921 
logit 921 : -0.3298538 
logit mean 921 : -0.4388029 
logit RMSE 921 : 0.07041738 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 921 : -0.3943503 
boosting mean 921 : -0.4833048 
boosting RMSE 921 : 0.1361738 

forest 921 : -0.2969081 
forest mean 921 : -0.3928909 
forest RMSE 921 : 0.05125023 

nnet 921 : -0.3731943 
nnet mean 921 : -0.4146761 
nnet RMSE 921 : 0.1522796 


s: 922 
logit 922 : -0.4563311 
logit mean 922 : -0.4388219 
logit RMSE 922 : 0.07040363 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 922 : -0.4731834 
boosting mean 922 : -0.4832939 
boosting RMSE 922 : 0.1361213 

forest 922 : -0.3982891 
forest mean 922 : -0.3928968 
forest RMSE 922 : 0.05122246 

nnet 922 : -0.447872 
nnet mean 922 : -0.4147121 
nnet RMSE 922 : 0.1522051 


s: 923 
logit 923 : -0.4762231 
logit mean 923 : -0.4388624 
logit RMSE 923 : 0.0704102 

boosting 923 : -0.4328671 
boosting mean 923 : -0.4832392 
boosting RMSE 923 : 0.1360518 

forest 923 : -0.3572045 
forest mean 923 : -0.3928581 
forest RMSE 923 : 0.05121408 

nnet 923 : -0.2736389 
nnet mean 923 : -0.4145593 
nnet RMSE 923 : 0.1521795 


s: 924 
logit 924 : -0.4109451 
logit mean 924 : -0.4388322 
logit RMSE 924 : 0.070373 

boosting 924 : -0.3733973 
boosting mean 924 : -0.4831204 
boosting RMSE 924 : 0.135981 

forest 924 : -0.3979905 
forest mean 924 : -0.3928637 
forest RMSE 924 : 0.05118641 

nnet 924 : -0.4504557 
nnet mean 924 : -0.4145981 
nnet RMSE 924 : 0.1521062 


s: 925 
logit 925 : -0.5530001 
logit mean 925 : -0.4389556 
logit RMSE 925 : 0.07051463 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 925 : -0.4430754 
boosting mean 925 : -0.4830771 
boosting RMSE 925 : 0.1359148 

forest 925 : -0.3417987 
forest mean 925 : -0.3928085 
forest RMSE 925 : 0.05119451 

nnet 925 : -0.4944856 
nnet mean 925 : -0.4146845 
nnet RMSE 925 : 0.1520557 


s: 926 
logit 926 : -0.401865 
logit mean 926 : -0.4389156 
logit RMSE 926 : 0.07047657 

boosting 926 : -0.4361056 
boosting mean 926 : -0.4830263 
boosting RMSE 926 : 0.1358466 

forest 926 : -0.423723 
forest mean 926 : -0.3928418 
forest RMSE 926 : 0.0511728 

nnet 926 : -0.2022165 
nnet mean 926 : -0.4144551 
nnet RMSE 926 : 0.1521125 


s: 927 
logit 927 : -0.3466625 
logit mean 927 : -0.438816 
logit RMSE 927 : 0.07046033 

boosting 927 : -0.3820671 
boosting mean 927 : -0.4829174 
boosting RMSE 927 : 0.1357746 

forest 927 : -0.3655761 
forest mean 927 : -0.3928124 
forest RMSE 927 : 0.05115768 

nnet 927 : -0.3754219 
nnet mean 927 : -0.4144129 
nnet RMSE 927 : 0.1520326 


s: 928 
logit 928 : -0.4610428 
logit mean 928 : -0.43884 
logit RMSE 928 : 0.07045086 

boosting 928 : -0.4839811 
boosting mean 928 : -0.4829186 
boosting RMSE 928 : 0.1357294 

forest 928 : -0.3671455 
forest mean 928 : -0.3927848 
forest RMSE 928 : 0.05114149 

nnet 928 : -0.3990144 
nnet mean 928 : -0.4143964 
nnet RMSE 928 : 0.1519506 


s: 929 
logit 929 : -0.3926325 
logit mean 929 : -0.4387902 
logit RMSE 929 : 0.07041335 

boosting 929 : -0.3234234 
boosting mean 929 : -0.4827469 
boosting RMSE 929 : 0.1356796 

forest 929 : -0.3638114 
forest mean 929 : -0.3927536 
forest RMSE 929 : 0.05112774 

nnet 929 : -0.4067458 
nnet mean 929 : -0.4143881 
nnet RMSE 929 : 0.1518690 


s: 930 
logit 930 : -0.5270479 
logit mean 930 : -0.4388851 
logit RMSE 930 : 0.07049868 

boosting 930 : -0.5775013 
boosting mean 930 : -0.4828488 
boosting RMSE 930 : 0.1357315 

forest 930 : -0.3613107 
forest mean 930 : -0.3927198 
forest RMSE 930 : 0.05111599 

nnet 930 : -0.04078743 
nnet mean 930 : -0.4139864 
nnet RMSE 930 : 0.1522437 


s: 931 
logit 931 : -0.45865 
logit mean 931 : -0.4389064 
logit RMSE 931 : 0.07048702 

boosting 931 : -0.478323 
boosting mean 931 : -0.4828439 
boosting RMSE 931 : 0.1356829 

forest 931 : -0.496421 
forest mean 931 : -0.3928312 
forest RMSE 931 : 0.05118617 

nnet 931 : -0.3931300 
nnet mean 931 : -0.413964 
nnet RMSE 931 : 0.1521620 


s: 932 
logit 932 : -0.4918726 
logit mean 932 : -0.4389632 
logit RMSE 932 : 0.07051345 

boosting 932 : -0.4614578 
boosting mean 932 : -0.482821 
boosting RMSE 932 : 0.135625 

forest 932 : -0.3794383 
forest mean 932 : -0.3928168 
forest RMSE 932 : 0.05116314 

nnet 932 : -0.4186506 
nnet mean 932 : -0.413969 
nnet RMSE 932 : 0.1520816 


s: 933 
logit 933 : -0.3848079 
logit mean 933 : -0.4389052 
logit RMSE 933 : 0.0704774 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 933 : -0.5105261 
boosting mean 933 : -0.4828507 
boosting RMSE 933 : 0.1356006 

forest 933 : -0.3426384 
forest mean 933 : -0.392763 
forest RMSE 933 : 0.05117018 

nnet 933 : -0.351075 
nnet mean 933 : -0.4139016 
nnet RMSE 933 : 0.1520085 


s: 934 
logit 934 : -0.4267087 
logit mean 934 : -0.4388921 
logit RMSE 934 : 0.07044508 

boosting 934 : -0.3981558 
boosting mean 934 : -0.48276 
boosting RMSE 934 : 0.135528 

forest 934 : -0.4954027 
forest mean 934 : -0.3928729 
forest RMSE 934 : 0.05123797 

nnet 934 : -0.346353 
nnet mean 934 : -0.4138293 
nnet RMSE 934 : 0.1519373 


s: 935 
logit 935 : -0.3491751 
logit mean 935 : -0.4387962 
logit RMSE 935 : 0.07042702 

boosting 935 : -0.295705 
boosting mean 935 : -0.4825599 
boosting RMSE 935 : 0.1354984 

forest 935 : -0.3610491 
forest mean 935 : -0.3928389 
forest RMSE 935 : 0.0512264 

nnet 935 : -0.422948 
nnet mean 935 : -0.413839 
nnet RMSE 935 : 0.1518579 


s: 936 
logit 936 : -0.4768771 
logit mean 936 : -0.4388368 
logit RMSE 936 : 0.07043423 

boosting 936 : -0.6399188 
boosting mean 936 : -0.482728 
boosting RMSE 936 : 0.1356529 

forest 936 : -0.4406254 
forest mean 936 : -0.3928899 
forest RMSE 936 : 0.05121624 

nnet 936 : -0.4434387 
nnet mean 936 : -0.4138707 
nnet RMSE 936 : 0.1517834 


s: 937 
logit 937 : -0.518571 
logit mean 937 : -0.4389219 
logit RMSE 937 : 0.07050312 

boosting 937 : -0.4136862 
boosting mean 937 : -0.4826544 
boosting RMSE 937 : 0.1355812 

forest 937 : -0.4129227 
forest mean 937 : -0.3929113 
forest RMSE 937 : 0.05119065 

nnet 937 : -0.5209377 
nnet mean 937 : -0.4139849 
nnet RMSE 937 : 0.1517538 


s: 938 
logit 938 : -0.3752709 
logit mean 938 : -0.4388541 
logit RMSE 938 : 0.07047015 

boosting 938 : -0.4140507 
boosting mean 938 : -0.4825812 
boosting RMSE 938 : 0.1355097 

forest 938 : -0.3587774 
forest mean 938 : -0.3928749 
forest RMSE 938 : 0.05118105 

nnet 938 : -0.5320718 
nnet mean 938 : -0.4141108 
nnet RMSE 938 : 0.1517342 


s: 939 
logit 939 : -0.4651705 
logit mean 939 : -0.4388821 
logit RMSE 939 : 0.07046472 

boosting 939 : -0.6257105 
boosting mean 939 : -0.4827336 
boosting RMSE 939 : 0.1356377 

forest 939 : -0.3787558 
forest mean 939 : -0.3928599 
forest RMSE 939 : 0.05115849 

nnet 939 : -0.3982997 
nnet mean 939 : -0.414094 
nnet RMSE 939 : 0.1516534 


s: 940 
logit 940 : -0.3617287 
logit mean 940 : -0.4388 
logit RMSE 940 : 0.0704383 

boosting 940 : -0.3454109 
boosting mean 940 : -0.4825876 
boosting RMSE 940 : 0.1355772 

forest 940 : -0.5166553 
forest mean 940 : -0.3929916 
forest RMSE 940 : 0.05127265 

nnet 940 : -0.4451583 
nnet mean 940 : -0.414127 
nnet RMSE 940 : 0.1515798 


s: 941 
logit 941 : -0.3756803 
logit mean 941 : -0.4387329 
logit RMSE 941 : 0.07040532 

boosting 941 : -0.3632351 
boosting mean 941 : -0.4824607 
boosting RMSE 941 : 0.1355105 

forest 941 : -0.2660677 
forest mean 941 : -0.3928567 
forest RMSE 941 : 0.05143105 

nnet 941 : -0.3724693 
nnet mean 941 : -0.4140828 
nnet RMSE 941 : 0.1515019 


s: 942 
logit 942 : -0.4604296 
logit mean 942 : -0.438756 
logit RMSE 942 : 0.07039548 

boosting 942 : -0.3749348 
boosting mean 942 : -0.4823466 
boosting RMSE 942 : 0.1354410 

forest 942 : -0.3421113 
forest mean 942 : -0.3928028 
forest RMSE 942 : 0.05143834 

nnet 942 : -0.4806791 
nnet mean 942 : -0.4141535 
nnet RMSE 942 : 0.1514443 


s: 943 
logit 943 : -0.4740977 
logit mean 943 : -0.4387935 
logit RMSE 943 : 0.07039951 

boosting 943 : -0.4223148 
boosting mean 943 : -0.4822829 
boosting RMSE 943 : 0.1353711 

forest 943 : -0.3296094 
forest mean 943 : -0.3927358 
forest RMSE 943 : 0.05146213 

nnet 943 : -0.3107133 
nnet mean 943 : -0.4140438 
nnet RMSE 943 : 0.1513919 


s: 944 
logit 944 : -0.533219 
logit mean 944 : -0.4388935 
logit RMSE 944 : 0.07049568 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 944 : -0.5234578 
boosting mean 944 : -0.4823265 
boosting RMSE 944 : 0.1353590 

forest 944 : -0.4057549 
forest mean 944 : -0.3927496 
forest RMSE 944 : 0.05143521 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 944 : -0.2388645 
nnet mean 944 : -0.4138582 
nnet RMSE 944 : 0.1514026 


s: 945 
logit 945 : -0.4251252 
logit mean 945 : -0.4388789 
logit RMSE 945 : 0.07046311 

boosting 945 : -0.5858326 
boosting mean 945 : -0.4824361 
boosting RMSE 945 : 0.1354224 

forest 945 : -0.3850158 
forest mean 945 : -0.3927414 
forest RMSE 945 : 0.0514103 

nnet 945 : -0.1271684 
nnet mean 945 : -0.4135548 
nnet RMSE 945 : 0.1515825 


s: 946 
logit 946 : -0.4950566 
logit mean 946 : -0.4389383 
logit RMSE 946 : 0.07049364 

boosting 946 : -0.4920778 
boosting mean 946 : -0.4824463 
boosting RMSE 946 : 0.1353839 

forest 946 : -0.4650268 
forest mean 946 : -0.3928178 
forest RMSE 946 : 0.05142659 

nnet 946 : -0.1371350 
nnet mean 946 : -0.4132626 
nnet RMSE 946 : 0.1517432 


s: 947 
logit 947 : -0.4403891 
logit mean 947 : -0.4389398 
logit RMSE 947 : 0.07046863 

boosting 947 : -0.4607545 
boosting mean 947 : -0.4824234 
boosting RMSE 947 : 0.1353268 

forest 947 : -0.2573541 
forest mean 947 : -0.3926748 
forest RMSE 947 : 0.05160803 

nnet 947 : -0.5405757 
nnet mean 947 : -0.4133971 
nnet RMSE 947 : 0.1517318 


s: 948 
logit 948 : -0.3393683 
logit mean 948 : -0.4388348 
logit RMSE 948 : 0.07045898 

boosting 948 : -0.3122864 
boosting mean 948 : -0.4822439 
boosting RMSE 948 : 0.1352854 

forest 948 : -0.3673264 
forest mean 948 : -0.3926480 
forest RMSE 948 : 0.05159172 

nnet 948 : -0.2798168 
nnet mean 948 : -0.4132562 
nnet RMSE 948 : 0.1517020 


s: 949 
logit 949 : -0.4553344 
logit mean 949 : -0.4388522 
logit RMSE 949 : 0.07044475 

boosting 949 : -0.4474826 
boosting mean 949 : -0.4822073 
boosting RMSE 949 : 0.1352229 

forest 949 : -0.4046641 
forest mean 949 : -0.3926607 
forest RMSE 949 : 0.05156475 

nnet 949 : -0.526169 
nnet mean 949 : -0.4133751 
nnet RMSE 949 : 0.1516774 


s: 950 
logit 950 : -0.381135 
logit mean 950 : -0.4387914 
logit RMSE 950 : 0.07041033 

boosting 950 : -0.5197169 
boosting mean 950 : -0.4822467 
boosting RMSE 950 : 0.1352075 

forest 950 : -0.5030288 
forest mean 950 : -0.3927769 
forest RMSE 950 : 0.05164589 

nnet 950 : -0.5079135 
nnet mean 950 : -0.4134746 
nnet RMSE 950 : 0.1516379 


s: 951 
logit 951 : -0.3411456 
logit mean 951 : -0.4386887 
logit RMSE 951 : 0.07039917 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 951 : -0.7304187 
boosting mean 951 : -0.4825077 
boosting RMSE 951 : 0.1355605 

forest 951 : -0.4326855 
forest mean 951 : -0.3928188 
forest RMSE 951 : 0.05162961 

nnet 951 : -0.914423 
nnet mean 951 : -0.4140014 
nnet RMSE 951 : 0.1524735 


s: 952 
logit 952 : -0.4980611 
logit mean 952 : -0.4387511 
logit RMSE 952 : 0.07043393 

boosting 952 : -0.5043836 
boosting mean 952 : -0.4825307 
boosting RMSE 952 : 0.1355315 

forest 952 : -0.4878395 
forest mean 952 : -0.3929187 
forest RMSE 952 : 0.05168096 

nnet 952 : -0.2452287 
nnet mean 952 : -0.4138241 
nnet RMSE 952 : 0.1524759 


s: 953 
logit 953 : -0.4057149 
logit mean 953 : -0.4387165 
logit RMSE 953 : 0.0703972 

boosting 953 : -0.4548291 
boosting mean 953 : -0.4825016 
boosting RMSE 953 : 0.1354720 

forest 953 : -0.4402979 
forest mean 953 : -0.3929684 
forest RMSE 953 : 0.05167033 

nnet 953 : -0.2670935 
nnet mean 953 : -0.4136702 
nnet RMSE 953 : 0.1524567 


s: 954 
logit 954 : -0.4817825 
logit mean 954 : -0.4387616 
logit RMSE 954 : 0.0704101 

boosting 954 : -0.3439681 
boosting mean 954 : -0.4823564 
boosting RMSE 954 : 0.1354131 

forest 954 : -0.3674799 
forest mean 954 : -0.3929417 
forest RMSE 954 : 0.05165397 

nnet 954 : -0.2193366 
nnet mean 954 : -0.4134665 
nnet RMSE 954 : 0.1524890 


s: 955 
logit 955 : -0.4103112 
logit mean 955 : -0.4387318 
logit RMSE 955 : 0.07037402 

boosting 955 : -0.5081778 
boosting mean 955 : -0.4823834 
boosting RMSE 955 : 0.1353875 

forest 955 : -0.3463063 
forest mean 955 : -0.3928928 
forest RMSE 955 : 0.05165615 

nnet 955 : -0.4638368 
nnet mean 955 : -0.4135192 
nnet RMSE 955 : 0.1524231 


s: 956 
logit 956 : -0.4236686 
logit mean 956 : -0.4387160 
logit RMSE 956 : 0.07034137 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 956 : -0.3999767 
boosting mean 956 : -0.4822972 
boosting RMSE 956 : 0.1353167 

forest 956 : -0.3490654 
forest mean 956 : -0.392847 
forest RMSE 956 : 0.0516554 

nnet 956 : -0.4987866 
nnet mean 956 : -0.4136084 
nnet RMSE 956 : 0.1523769 


s: 957 
logit 957 : -0.4681609 
logit mean 957 : -0.4387468 
logit RMSE 957 : 0.07033913 

boosting 957 : -0.1861021 
boosting mean 957 : -0.4819877 
boosting RMSE 957 : 0.1354226 

forest 957 : -0.4409047 
forest mean 957 : -0.3928972 
forest RMSE 957 : 0.05164533 

nnet 957 : -0.5910861 
nnet mean 957 : -0.4137938 
nnet RMSE 957 : 0.1524224 


s: 958 
logit 958 : -0.4739987 
logit mean 958 : -0.4387836 
logit RMSE 958 : 0.07034305 

boosting 958 : -0.4500595 
boosting mean 958 : -0.4819544 
boosting RMSE 958 : 0.1353615 

forest 958 : -0.2610106 
forest mean 958 : -0.3927595 
forest RMSE 958 : 0.05181333 

nnet 958 : -0.3621782 
nnet mean 958 : -0.41374 
nnet RMSE 958 : 0.1523478 


s: 959 
logit 959 : -0.4081612 
logit mean 959 : -0.4387517 
logit RMSE 959 : 0.07030686 

boosting 959 : -0.5994185 
boosting mean 959 : -0.4820769 
boosting RMSE 959 : 0.1354441 

forest 959 : -0.4221032 
forest mean 959 : -0.3927901 
forest RMSE 959 : 0.05179123 

nnet 959 : -0.6630497 
nnet mean 959 : -0.4139999 
nnet RMSE 959 : 0.1525051 


s: 960 
logit 960 : -0.3374849 
logit mean 960 : -0.4386462 
logit RMSE 960 : 0.07029919 

boosting 960 : -0.478279 
boosting mean 960 : -0.4820729 
boosting RMSE 960 : 0.1353971 

forest 960 : -0.3447235 
forest mean 960 : -0.3927401 
forest RMSE 960 : 0.05179498 

nnet 960 : -0.4587391 
nnet mean 960 : -0.4140465 
nnet RMSE 960 : 0.1524374 


s: 961 
logit 961 : -0.4952161 
logit mean 961 : -0.4387051 
logit RMSE 961 : 0.0703297 

boosting 961 : -0.4560507 
boosting mean 961 : -0.4820459 
boosting RMSE 961 : 0.1353387 

forest 961 : -0.3512789 
forest mean 961 : -0.3926969 
forest RMSE 961 : 0.05179188 

nnet 961 : -0.4881473 
nnet mean 961 : -0.4141236 
nnet RMSE 961 : 0.1523846 


s: 962 
logit 962 : -0.318238 
logit mean 962 : -0.4385798 
logit RMSE 962 : 0.07034255 

boosting 962 : -0.2154542 
boosting mean 962 : -0.4817687 
boosting RMSE 962 : 0.1353992 

forest 962 : -0.3279425 
forest mean 962 : -0.3926296 
forest RMSE 962 : 0.05181706 

nnet 962 : -0.1014289 
nnet mean 962 : -0.4137986 
nnet RMSE 962 : 0.1526093 


s: 963 
logit 963 : -0.52889 
logit mean 963 : -0.4386736 
logit RMSE 963 : 0.0704286 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 963 : -0.8255487 
boosting mean 963 : -0.4821257 
boosting RMSE 963 : 0.1360219 

forest 963 : -0.3560724 
forest mean 963 : -0.3925916 
forest RMSE 963 : 0.05180949 

nnet 963 : -0.460817 
nnet mean 963 : -0.4138474 
nnet RMSE 963 : 0.1525426 


s: 964 
logit 964 : -0.4353775 
logit mean 964 : -0.4386702 
logit RMSE 964 : 0.07040128 

boosting 964 : -0.4686958 
boosting mean 964 : -0.4821118 
boosting RMSE 964 : 0.1359693 

forest 964 : -0.5004849 
forest mean 964 : -0.3927036 
forest RMSE 964 : 0.05188365 

nnet 964 : -0.3258809 
nnet mean 964 : -0.4137562 
nnet RMSE 964 : 0.1524822 


s: 965 
logit 965 : -0.404273 
logit mean 965 : -0.4386345 
logit RMSE 965 : 0.07036493 

boosting 965 : -0.3911489 
boosting mean 965 : -0.4820175 
boosting RMSE 965 : 0.1358991 

forest 965 : -0.3579332 
forest mean 965 : -0.3926675 
forest RMSE 965 : 0.05187444 

nnet 965 : -0.6434746 
nnet mean 965 : -0.4139942 
nnet RMSE 965 : 0.1526045 


s: 966 
logit 966 : -0.3546244 
logit mean 966 : -0.4385476 
logit RMSE 966 : 0.07034365 

boosting 966 : -0.4271703 
boosting mean 966 : -0.4819607 
boosting RMSE 966 : 0.1358316 

forest 966 : -0.4351804 
forest mean 966 : -0.3927115 
forest RMSE 966 : 0.05185994 

nnet 966 : -0.5868307 
nnet mean 966 : -0.4141731 
nnet RMSE 966 : 0.1526439 


s: 967 
logit 967 : -0.4268608 
logit mean 967 : -0.4385355 
logit RMSE 967 : 0.07031258 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 967 : -0.4747074 
boosting mean 967 : -0.4819532 
boosting RMSE 967 : 0.1357826 

forest 967 : -0.3762858 
forest mean 967 : -0.3926946 
forest RMSE 967 : 0.05183872 

nnet 967 : -0.3560392 
nnet mean 967 : -0.414113 
nnet RMSE 967 : 0.1525716 


s: 968 
logit 968 : -0.4511171 
logit mean 968 : -0.4385485 
logit RMSE 968 : 0.07029545 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 968 : -0.4889681 
boosting mean 968 : -0.4819605 
boosting RMSE 968 : 0.1357426 

forest 968 : -0.3496910 
forest mean 968 : -0.3926501 
forest RMSE 968 : 0.05183717 

nnet 968 : -0.360887 
nnet mean 968 : -0.414058 
nnet RMSE 968 : 0.1524979 


s: 969 
logit 969 : -0.4875217 
logit mean 969 : -0.438599 
logit RMSE 969 : 0.0703154 

boosting 969 : -0.4601076 
boosting mean 969 : -0.4819379 
boosting RMSE 969 : 0.1356862 

forest 969 : -0.4584888 
forest mean 969 : -0.3927181 
forest RMSE 969 : 0.05184447 

nnet 969 : -0.4975241 
nnet mean 969 : -0.4141442 
nnet RMSE 969 : 0.1524514 


s: 970 
logit 970 : -0.4772945 
logit mean 970 : -0.4386389 
logit RMSE 970 : 0.07032296 

boosting 970 : -0.6824179 
boosting mean 970 : -0.4821446 
boosting RMSE 970 : 0.1359191 

forest 970 : -0.3588395 
forest mean 970 : -0.3926831 
forest RMSE 970 : 0.05183459 

nnet 970 : -0.6100886 
nnet mean 970 : -0.4143462 
nnet RMSE 970 : 0.1525220 


s: 971 
logit 971 : -0.4124785 
logit mean 971 : -0.438612 
logit RMSE 971 : 0.07028788 

boosting 971 : -0.490903 
boosting mean 971 : -0.4821536 
boosting RMSE 971 : 0.1358804 

forest 971 : -0.5180369 
forest mean 971 : -0.3928122 
forest RMSE 971 : 0.05194619 

nnet 971 : -0.4615214 
nnet mean 971 : -0.4143948 
nnet RMSE 971 : 0.1524563 


s: 972 
logit 972 : -0.4225793 
logit mean 972 : -0.4385955 
logit RMSE 972 : 0.07025544 

boosting 972 : -0.5878522 
boosting mean 972 : -0.4822624 
boosting RMSE 972 : 0.1359441 

forest 972 : -0.449982 
forest mean 972 : -0.3928711 
forest RMSE 972 : 0.05194421 

nnet 972 : -0.2982586 
nnet mean 972 : -0.4142753 
nnet RMSE 972 : 0.1524127 


s: 973 
logit 973 : -0.3964698 
logit mean 973 : -0.4385522 
logit RMSE 973 : 0.07021942 

boosting 973 : -0.421719 
boosting mean 973 : -0.4822002 
boosting RMSE 973 : 0.135876 

forest 973 : -0.3571709 
forest mean 973 : -0.3928344 
forest RMSE 973 : 0.05193566 

nnet 973 : -0.5421338 
nnet mean 973 : -0.4144067 
nnet RMSE 973 : 0.1524025 


s: 974 
logit 974 : -0.4713614 
logit mean 974 : -0.4385859 
logit RMSE 974 : 0.0702206 

boosting 974 : -0.466793 
boosting mean 974 : -0.4821843 
boosting RMSE 974 : 0.1358231 

forest 974 : -0.3835819 
forest mean 974 : -0.3928249 
forest RMSE 974 : 0.05191166 

nnet 974 : -0.3429258 
nnet mean 974 : -0.4143333 
nnet RMSE 974 : 0.1523353 


s: 975 
logit 975 : -0.4147537 
logit mean 975 : -0.4385614 
logit RMSE 975 : 0.07018617 

boosting 975 : -0.4881484 
boosting mean 975 : -0.4821905 
boosting RMSE 975 : 0.1357828 

forest 975 : -0.3206018 
forest mean 975 : -0.3927508 
forest RMSE 975 : 0.0519473 

nnet 975 : -0.2954247 
nnet mean 975 : -0.4142113 
nnet RMSE 975 : 0.1522940 


s: 976 
logit 976 : -0.4904393 
logit mean 976 : -0.4386146 
logit RMSE 976 : 0.07020992 

boosting 976 : -0.5436806 
boosting mean 976 : -0.4822535 
boosting RMSE 976 : 0.1357911 

forest 976 : -0.4087213 
forest mean 976 : -0.3927672 
forest RMSE 976 : 0.05192143 

nnet 976 : -0.5246394 
nnet mean 976 : -0.4143245 
nnet RMSE 976 : 0.1522682 


s: 977 
logit 977 : -0.4038541 
logit mean 977 : -0.438579 
logit RMSE 977 : 0.07017408 

boosting 977 : -0.4375536 
boosting mean 977 : -0.4822077 
boosting RMSE 977 : 0.1357269 

forest 977 : -0.5031967 
forest mean 977 : -0.3928802 
forest RMSE 977 : 0.05199977 

nnet 977 : -0.2569714 
nnet mean 977 : -0.4141634 
nnet RMSE 977 : 0.1522590 


s: 978 
logit 978 : -0.3606407 
logit mean 978 : -0.4384993 
logit RMSE 978 : 0.07014949 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 978 : -0.4447075 
boosting mean 978 : -0.4821694 
boosting RMSE 978 : 0.1356650 

forest 978 : -0.3024559 
forest mean 978 : -0.3927877 
forest RMSE 978 : 0.05206669 

nnet 978 : -0.2260550 
nnet mean 978 : -0.4139711 
nnet RMSE 978 : 0.1522828 


s: 979 
logit 979 : -0.4514148 
logit mean 979 : -0.4385125 
logit RMSE 979 : 0.0701329 

boosting 979 : -0.4673714 
boosting mean 979 : -0.4821543 
boosting RMSE 979 : 0.1356128 

forest 979 : -0.3038105 
forest mean 979 : -0.3926968 
forest RMSE 979 : 0.05213081 

nnet 979 : -0.4025003 
nnet mean 979 : -0.4139594 
nnet RMSE 979 : 0.152205 


s: 980 
logit 980 : -0.3786485 
logit mean 980 : -0.4384514 
logit RMSE 980 : 0.07010043 

boosting 980 : -0.4045079 
boosting mean 980 : -0.482075 
boosting RMSE 980 : 0.1355437 

forest 980 : -0.3511056 
forest mean 980 : -0.3926544 
forest RMSE 980 : 0.05212761 

nnet 980 : -0.3227026 
nnet mean 980 : -0.4138662 
nnet RMSE 980 : 0.1521474 


s: 981 
logit 981 : -0.4808215 
logit mean 981 : -0.4384946 
logit RMSE 981 : 0.0701122 

boosting 981 : -0.5167512 
boosting mean 981 : -0.4821104 
boosting RMSE 981 : 0.1355259 

forest 981 : -0.4321089 
forest mean 981 : -0.3926946 
forest RMSE 981 : 0.05211112 

nnet 981 : -0.5093959 
nnet mean 981 : -0.4139636 
nnet RMSE 981 : 0.1521099 


s: 982 
logit 982 : -0.3430603 
logit mean 982 : -0.4383974 
logit RMSE 982 : 0.07010004 

boosting 982 : -0.2884273 
boosting mean 982 : -0.4819131 
boosting RMSE 982 : 0.1355036 

forest 982 : -0.2651754 
forest mean 982 : -0.3925648 
forest RMSE 982 : 0.05226198 

nnet 982 : -0.2372635 
nnet mean 982 : -0.4137837 
nnet RMSE 982 : 0.1521211 


s: 983 
logit 983 : -0.4814414 
logit mean 983 : -0.4384412 
logit RMSE 983 : 0.07011251 

boosting 983 : -0.3840477 
boosting mean 983 : -0.4818136 
boosting RMSE 983 : 0.1354356 

forest 983 : -0.4294496 
forest mean 983 : -0.3926023 
forest RMSE 983 : 0.05224384 

nnet 983 : -0.4704124 
nnet mean 983 : -0.4138413 
nnet RMSE 983 : 0.1520603 


s: 984 
logit 984 : -0.4335216 
logit mean 984 : -0.4384362 
logit RMSE 984 : 0.07008502 

boosting 984 : -0.3601682 
boosting mean 984 : -0.48169 
boosting RMSE 984 : 0.1353728 

forest 984 : -0.3926415 
forest mean 984 : -0.3926023 
forest RMSE 984 : 0.05221781 

nnet 984 : -0.6900665 
nnet mean 984 : -0.414122 
nnet RMSE 984 : 0.1522640 


s: 985 
logit 985 : -0.4811461 
logit mean 985 : -0.4384796 
logit RMSE 985 : 0.07009714 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 985 : -0.6673023 
boosting mean 985 : -0.4818784 
boosting RMSE 985 : 0.1355718 

forest 985 : -0.3914190 
forest mean 985 : -0.3926011 
forest RMSE 985 : 0.05219201 

nnet 985 : -0.5817063 
nnet mean 985 : -0.4142922 
nnet RMSE 985 : 0.1522968 


s: 986 
logit 986 : -0.3789219 
logit mean 986 : -0.4384192 
logit RMSE 986 : 0.0700648 

boosting 986 : -0.2690319 
boosting mean 986 : -0.4816625 
boosting RMSE 986 : 0.1355672 

forest 986 : -0.3823395 
forest mean 986 : -0.3925907 
forest RMSE 986 : 0.05216857 
Increasing memory because of ties: allocating a matrix of size 3 times 200000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.
Increasing memory because of ties: allocating a matrix of size 3 times 300000 doubles.
I would be faster with the ties=FALSE option.
Warning in MatchLoopCfast(N = s1$N, xvars = Kx, All = s1$All, M = s1$M,  :
  Increasing memory because of ties.  I would be faster with the ties=FALSE option.

nnet 986 : -0.3246868 
nnet mean 986 : -0.4142013 
nnet RMSE 986 : 0.1522385 


s: 987 
logit 987 : -0.3919202 
logit mean 987 : -0.4383721 
logit RMSE 987 : 0.07002977 

boosting 987 : -0.3636861 
boosting mean 987 : -0.481543 
boosting RMSE 987 : 0.1355035 

forest 987 : -0.4012905 
forest mean 987 : -0.3925995 
forest RMSE 987 : 0.05214215 

nnet 987 : -0.3832537 
nnet mean 987 : -0.4141699 
nnet RMSE 987 : 0.1521623 


s: 988 
logit 988 : -0.4351139 
logit mean 988 : -0.4383688 
logit RMSE 988 : 0.07000323 

boosting 988 : -0.530517 
boosting mean 988 : -0.4815926 
boosting RMSE 988 : 0.1354985 

forest 988 : -0.3208681 
forest mean 988 : -0.3925269 
forest RMSE 988 : 0.05217653 

nnet 988 : -0.1246542 
nnet mean 988 : -0.4138769 
nnet RMSE 988 : 0.1523373 


s: 989 
logit 989 : -0.4946951 
logit mean 989 : -0.4384257 
logit RMSE 989 : 0.0700326 

boosting 989 : -0.4721177 
boosting mean 989 : -0.481583 
boosting RMSE 989 : 0.1354494 

forest 989 : -0.3650362 
forest mean 989 : -0.3924991 
forest RMSE 989 : 0.05216199 

nnet 989 : -0.3030179 
nnet mean 989 : -0.4137648 
nnet RMSE 989 : 0.1522915 


s: 990 
logit 990 : -0.3985681 
logit mean 990 : -0.4383855 
logit RMSE 990 : 0.06999723 

boosting 990 : -0.4021934 
boosting mean 990 : -0.4815028 
boosting RMSE 990 : 0.135381 

forest 990 : -0.3661045 
forest mean 990 : -0.3924725 
forest RMSE 990 : 0.05214677 

nnet 990 : -0.4389505 
nnet mean 990 : -0.4137902 
nnet RMSE 990 : 0.1522196 


s: 991 
logit 991 : -0.4190397 
logit mean 991 : -0.4383659 
logit RMSE 991 : 0.06996452 

boosting 991 : -0.5470699 
boosting mean 991 : -0.481569 
boosting RMSE 991 : 0.1353933 

forest 991 : -0.4439873 
forest mean 991 : -0.3925245 
forest RMSE 991 : 0.05213918 

nnet 991 : -0.268844 
nnet mean 991 : -0.413644 
nnet RMSE 991 : 0.1521998 


s: 992 
logit 992 : -0.4842112 
logit mean 992 : -0.4384122 
logit RMSE 992 : 0.06998034 

boosting 992 : -0.2343114 
boosting mean 992 : -0.4813197 
boosting RMSE 992 : 0.1354272 

forest 992 : -0.3416481 
forest mean 992 : -0.3924732 
forest RMSE 992 : 0.05214582 

nnet 992 : -0.4349057 
nnet mean 992 : -0.4136654 
nnet RMSE 992 : 0.1521271 


s: 993 
logit 993 : -0.4181669 
logit mean 993 : -0.4383918 
logit RMSE 993 : 0.06994747 

boosting 993 : -0.3322904 
boosting mean 993 : -0.4811696 
boosting RMSE 993 : 0.1353761 

forest 993 : -0.4509389 
forest mean 993 : -0.3925320 
forest RMSE 993 : 0.05214462 

nnet 993 : -0.6297418 
nnet mean 993 : -0.413883 
nnet RMSE 993 : 0.1522252 


s: 994 
logit 994 : -0.3823013 
logit mean 994 : -0.4383353 
logit RMSE 994 : 0.06991453 

boosting 994 : -0.2952162 
boosting mean 994 : -0.4809825 
boosting RMSE 994 : 0.1353488 

forest 994 : -0.4118754 
forest mean 994 : -0.3925515 
forest RMSE 994 : 0.05211974 

nnet 994 : -0.4734594 
nnet mean 994 : -0.4139429 
nnet RMSE 994 : 0.1521664 


s: 995 
logit 995 : -0.4594202 
logit mean 995 : -0.4383565 
logit RMSE 995 : 0.06990478 
Warning in ps(z.a ~ w1 + w2 + w3 + w4 + w5 + w6 + w7 + w8 + w9 + w10, data = sdta,  :
  Optimal number of iterations is close to the specified n.trees. n.trees is likely set too small and better balance might be obtainable by setting n.trees to be larger.

boosting 995 : -0.3151111 
boosting mean 995 : -0.4808158 
boosting RMSE 995 : 0.1353075 

forest 995 : -0.3562153 
forest mean 995 : -0.392515 
forest RMSE 995 : 0.05211203 

nnet 995 : -0.3393264 
nnet mean 995 : -0.4138679 
nnet RMSE 995 : 0.1521021 


s: 996 
logit 996 : -0.4260440 
logit mean 996 : -0.4383442 
logit RMSE 996 : 0.06987455 

boosting 996 : -0.5139575 
boosting mean 996 : -0.4808491 
boosting RMSE 996 : 0.1352878 

forest 996 : -0.3705597 
forest mean 996 : -0.3924929 
forest RMSE 996 : 0.05209422 

nnet 996 : -0.3974702 
nnet mean 996 : -0.4138515 
nnet RMSE 996 : 0.1520258 


s: 997 
logit 997 : -0.4915903 
logit mean 997 : -0.4383976 
logit RMSE 997 : 0.06989971 

boosting 997 : -0.5024227 
boosting mean 997 : -0.4808708 
boosting RMSE 997 : 0.1352588 

forest 997 : -0.3514364 
forest mean 997 : -0.3924518 
forest RMSE 997 : 0.0520908 

nnet 997 : -0.3213755 
nnet mean 997 : -0.4137587 
nnet RMSE 997 : 0.1519699 


s: 998 
logit 998 : -0.4003562 
logit mean 998 : -0.4383595 
logit RMSE 998 : 0.06986468 

boosting 998 : -0.418924 
boosting mean 998 : -0.4808087 
boosting RMSE 998 : 0.1351924 

forest 998 : -0.4867724 
forest mean 998 : -0.3925463 
forest RMSE 998 : 0.0521371 

nnet 998 : -0.4964612 
nnet mean 998 : -0.4138416 
nnet RMSE 998 : 0.1519244 


s: 999 
logit 999 : -0.4353385 
logit mean 999 : -0.4383564 
logit RMSE 999 : 0.06983866 

boosting 999 : -0.547175 
boosting mean 999 : -0.4808751 
boosting RMSE 999 : 0.1352049 

forest 999 : -0.4481445 
forest mean 999 : -0.3926019 
forest RMSE 999 : 0.05213325 

nnet 999 : -0.5717125 
nnet mean 999 : -0.4139996 
nnet RMSE 999 : 0.1519455 


s: 1000 
logit 1000 : -0.4097073 
logit mean 1000 : -0.4383278 
logit RMSE 1000 : 0.0698044 

boosting 1000 : -0.5651462 
boosting mean 1000 : -0.4809594 
boosting RMSE 1000 : 0.1352381 

forest 1000 : -0.3708824 
forest mean 1000 : -0.3925802 
forest RMSE 1000 : 0.05211531 

nnet 1000 : -0.3154378 
nnet mean 1000 : -0.4139011 
nnet RMSE 1000 : 0.1518931 
> 
> 
> rm(sdta)
> save.image("RData.sims.E.nobs1000",compress="bzip2")
> 
> proc.time()
    user   system  elapsed 
47679.85    14.86 89370.50 
